-/*

- * Licensed to the Apache Software Foundation (ASF) under one or more

- * contributor license agreements. See the NOTICE file distributed with

- * this work for additional information regarding copyright ownership.

- * The ASF licenses this file to You under the Apache License, Version 2.0

- * (the "License"); you may not use this file except in compliance with

- * the License. You may obtain a copy of the License at

- *

- * http://www.apache.org/licenses/LICENSE-2.0

- *

- * Unless required by applicable law or agreed to in writing, software

- * distributed under the License is distributed on an "AS IS" BASIS,

- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

- * See the License for the specific language governing permissions and

- * limitations under the License.

- */

-package org.apache.commons.math;

-

-import java.io.PrintStream;

-import java.io.PrintWriter;

-import java.text.MessageFormat;

-import java.util.Locale;

-import java.util.MissingResourceException;

-import java.util.ResourceBundle;

-

-

-/**

-* Base class for commons-math checked exceptions.

-* <p>

-* Supports nesting, emulating JDK 1.4 behavior if necessary.</p>

-* <p>

-* Adapted from {@link org.apache.commons.collections.FunctorException}.</p>

-*

-* @version $Revision: 620312 $ $Date: 2008-02-10 12:28:59 -0700 (Sun, 10 Feb 2008) $

-*/

-public class MathException extends Exception {

-

- /** Serializable version identifier */

- private static final long serialVersionUID = -8602234299177097102L;

-

- /**

- * Does JDK support nested exceptions?

- */

- private static final boolean JDK_SUPPORTS_NESTED = true;

-

- /** Cache for resources bundle. */

- private static ResourceBundle cachedResources = null;

-

- /**

- * Pattern used to build the message.

- */

- private final String pattern;

-

- /**

- * Arguments used to build the message.

- */

- private final Object[] arguments;

-

- /**

- * Root cause of the exception

- */

- private final Throwable rootCause;

-

- /**

- * Translate a string to a given locale.

- * @param s string to translate

- * @param locale locale into which to translate the string

- * @return translated string or original string

- * for unsupported locales or unknown strings

- */

- private static String translate(String s, Locale locale) {

- try {

- if ((cachedResources == null) || (! cachedResources.getLocale().equals(locale))) {

- // caching the resource bundle

- cachedResources =

- ResourceBundle.getBundle("org.apache.commons.math.MessagesResources", locale);

- }

-

- if (cachedResources.getLocale().getLanguage().equals(locale.getLanguage())) {

- // the value of the resource is the translated string

- return cachedResources.getString(s);

- }

-

- } catch (MissingResourceException mre) {

- // do nothing here

- }

-

- // the locale is not supported or the resource is unknown

- // don't translate and fall back to using the string as is

- return s;

-

- }

-

- /**

- * Builds a message string by from a pattern and its arguments.

- * @param pattern format specifier

- * @param arguments format arguments

- * @param locale Locale in which the message should be translated

- * @return a message string

- */

- private static String buildMessage(String pattern, Object[] arguments, Locale locale) {

- // do it the hard way, for Java 1.3. compatibility

- MessageFormat mf = new MessageFormat(translate(pattern, locale));

- mf.setLocale(locale);

- return mf.format(arguments);

- }

-

- /**

- * Constructs a new <code>MathException</code> with no

- * detail message.

- */

- public MathException() {

- super();

- this.pattern = null;

- this.arguments = new Object[0];

- this.rootCause = null;

- }

-

- /**

- * Constructs a new <code>MathException</code> with specified

- * detail message.

- *

- * @param msg the error message.

- * @deprecated as of 1.2, replaced by {@link #MathException(String, Object[])}

- */

- public MathException(String msg) {

- super(msg);

- this.pattern = msg;

- this.arguments = new Object[0];

- this.rootCause = null;

- }

-

- /**

- * Constructs a new <code>MathException</code> with specified

- * formatted detail message.

- * Message formatting is delegated to {@link java.text.MessageFormat}.

- * @param pattern format specifier

- * @param arguments format arguments

- */

- public MathException(String pattern, Object[] arguments) {

- super(buildMessage(pattern, arguments, Locale.US));

- this.pattern = pattern;

- this.arguments = (Object[]) arguments.clone();

- this.rootCause = null;

- }

-

- /**

- * Constructs a new <code>MathException</code> with specified

- * nested <code>Throwable</code> root cause.

- *

- * @param rootCause the exception or error that caused this exception

- * to be thrown.

- */

- public MathException(Throwable rootCause) {

- super((rootCause == null ? null : rootCause.getMessage()));

- this.pattern = getMessage();

- this.arguments = new Object[0];

- this.rootCause = rootCause;

- }

-

- /**

- * Constructs a new <code>MathException</code> with specified

- * detail message and nested <code>Throwable</code> root cause.

- *

- * @param msg the error message.

- * @param rootCause the exception or error that caused this exception

- * to be thrown.

- * @deprecated as of 1.2, replaced by {@link #MathException(String, Object[], Throwable)}

- */

- public MathException(String msg, Throwable rootCause) {

- super(msg);

- this.pattern = msg;

- this.arguments = new Object[0];

- this.rootCause = rootCause;

- }

-

- /**

- * Constructs a new <code>MathException</code> with specified

- * formatted detail message and nested <code>Throwable</code> root cause.

- * Message formatting is delegated to {@link java.text.MessageFormat}.

- * @param pattern format specifier

- * @param arguments format arguments

- * @param rootCause the exception or error that caused this exception

- * to be thrown.

- * @since 1.2

- */

- public MathException(String pattern, Object[] arguments, Throwable rootCause) {

- super(buildMessage(pattern, arguments, Locale.US));

- this.pattern = pattern;

- this.arguments = (Object[]) arguments.clone();

- this.rootCause = rootCause;

- }

-

- /** Gets the pattern used to build the message of this throwable.

- *

- * @return the pattern used to build the message of this throwable

- * @since 1.2

- */

- public String getPattern() {

- return pattern;

- }

-

- /** Gets the arguments used to build the message of this throwable.

- *

- * @return the arguments used to build the message of this throwable

- * @since 1.2

- */

- public Object[] getArguments() {

- return (Object[]) arguments.clone();

- }

-

- /** Gets the message in a specified locale.

- *

- * @param locale Locale in which the message should be translated

- *

- * @return localized message

- * @since 1.2

- */

- public String getMessage(Locale locale) {

- return (pattern == null) ? null : buildMessage(pattern, arguments, locale);

- }

-

- /**

- * Gets the cause of this throwable.

- *

- * @return the cause of this throwable, or <code>null</code>

- */

- public Throwable getCause() {

- return rootCause;

- }

-

- /**

- * Prints the stack trace of this exception to the standard error stream.

- */

- public void printStackTrace() {

- printStackTrace(System.err);

- }

-

- /**

- * Prints the stack trace of this exception to the specified stream.

- *

- * @param out the <code>PrintStream</code> to use for output

- */

- public void printStackTrace(PrintStream out) {

- synchronized (out) {

- PrintWriter pw = new PrintWriter(out, false);

- printStackTrace(pw);

- // Flush the PrintWriter before it's GC'ed.

- pw.flush();

- }

- }

-

- /**

- * Prints the stack trace of this exception to the specified writer.

- *

- * @param out the <code>PrintWriter</code> to use for output

- */

- public void printStackTrace(PrintWriter out) {

- synchronized (out) {

- super.printStackTrace(out);

- if (rootCause != null && JDK_SUPPORTS_NESTED == false) {

- out.print("Caused by: ");

- rootCause.printStackTrace(out);

- }

- }

- }

-

-}

-package org.singinst.uf.common;

-

-

-public class LogUtil {

-

- public static void error(Exception e) {

- e.printStackTrace();

-

- }

-

- public static void info(String string) {

- System.err.println(string);

- }

-

-}

-package org.singinst.uf.common;

-

-import java.util.Collection;

-

-public class StringUtil {

- public static String join(String delimiter, Collection<String> collection) {

- StringBuilder builder = new StringBuilder();

- boolean first = true;

- for (String string : collection) {

- if (!first) {

- builder.append(delimiter);

- }

- first = false;

- builder.append(string);

- }

- return builder.toString();

- }

-}

-package org.singinst.uf.math;

-

-public abstract class InvertableFunction {

-

- public static final InvertableFunction IDENTITY = new InvertableFunction() {

-

- @Override

- public double apply(double x) {

- return x;

- }

-

- @Override

- public double invert(double y) {

- return y;

- }

-

- };

-

- public abstract double apply(double x);

- public abstract double invert(double y);

-

- public InvertableFunction invert() {

- return new InvertedFunction(this);

- }

-

-}

-package org.singinst.uf.math;

-

-public class InvertedFunction extends InvertableFunction {

-

- private final InvertableFunction originalFunction;

-

- public InvertedFunction(InvertableFunction originalFunction) {

- this.originalFunction = originalFunction;

- }

-

- @Override

- public double apply(double x) {

- return originalFunction.invert(x);

- }

-

- @Override

- public double invert(double y) {

- return originalFunction.apply(y);

- }

-

- @Override

- public InvertableFunction invert() {

- return originalFunction;

- }

-}

-package org.singinst.uf.math;

-

-import org.singinst.uf.presenter.LineBounds;

-

-public class LinearTransform extends InvertableFunction {

-

- private final double scale;

- private final double offset;

-

- public LinearTransform(LineBounds from, LineBounds to) {

- scale = to.getLength() / from.getLength();

- offset = to.getFirst() - scale * from.getFirst();

- assert apply(from.getFirst()) == to.getFirst();

- assert apply(from.getSecond()) == to.getSecond();

- }

-

- @Override

- public final double apply(double x) {

- return scale * x + offset;

- }

-

- @Override

- public double invert(double y) {

- return (y - offset) / scale;

- }

-

-}

-package org.singinst.uf.math;

-

-import java.math.BigDecimal;

-import java.math.RoundingMode;

-import java.util.Random;

-

-import org.apache.commons.math.MathException;

-import org.apache.commons.math.distribution.NormalDistributionImpl;

-import org.apache.commons.math.special.Erf;

-

-public class MathUtil {

-

- public static double[] unwrapDoubleList(java.util.List<Double> l) {

- double[] retVal = new double[l.size()];

- for (int i=0; i<retVal.length; ++i) retVal[i] = l.get(i);

- return retVal;

- }

- public static java.util.ArrayList<Double> wrapDoubleArray(double[] a) {

- java.util.ArrayList<Double> retVal = new java.util.ArrayList<Double>(a.length);

- for (int i=0; i<a.length; ++i) retVal.add(a[i]);

- return retVal;

- }

-

- public static double clog(double p) {

- return -Math.log1p(-p);

- }

- public static double expc(double q) {

- return -Math.expm1(-q);

- }

-

- public static double logit(double p) {

- if (p<0 || p>1) { return Double.NaN; }

- if (p==0) { return Double.NEGATIVE_INFINITY; }

- if (p==1) { return Double.POSITIVE_INFINITY; }

- // if (p>0.25) { return Math.atanh(p*2-1)*2;...? oh }

- return Math.log(p/(1-p));

- }

- public static double logistic(double t) {

- return 1/(1+Math.exp(-t));

- }

-

- public static double log_logistic(double t) {

- //return Math.log(logistic(t));

- if (t <= -38) {

- return t;

- } else if (t > 38) {

- return -Math.exp(-t);

- }

- if (t<0) {

- return t - Math.log1p(Math.exp(t));

- } else {

- double r = Math.exp(-t);

- return Math.log1p(-r/(1+r));

- }

- }

- public static double logit_exp(double b) {

- //return logit(Math.exp(b));

- return b - Math.log(-Math.expm1(b));

- }

- public static double clog_logistic(double t) {

- return -log_logistic(-t);

- }

- public static double logit_expc(double q) {

- //return logit(expc(q));

- return q + Math.log(-Math.expm1(-q));

- }

-

- public static final double DOUBLE_EPS = 1-(1-(double)1.2e-16);

- static final double SQRT_ONE_OVER_EIGHT_PI = Math.sqrt(1/(8*Math.PI));

- static final double SQRT_ONE_HALF = Math.sqrt(0.5);

- public static double cumulativeNormalDistribution(double mean,

- double stdDev, double x) {

- try {

- double Z = (x - mean)/stdDev;

- if (Z<=-38.5) {

- return 0;

- } else if (Z>8.3) {

- return 1;

- } else if (Z<-1.15) {

- // absTol = DOUBLE_EPS/-Z because lg2(Z) mantissa bits in Z are lost as exponent bits in exp(-Z*Z/2)

- return SQRT_ONE_OVER_EIGHT_PI * normalTailCF(Z*Z/2, DOUBLE_EPS/-Z) * (-Z) * Math.exp(-Z*Z/2);

- } else if (Z>2) {

- double absTol = Math.exp(Z*Z/2)*DOUBLE_EPS/Z;

- return 1 - SQRT_ONE_OVER_EIGHT_PI * normalTailCF(Z*Z/2, absTol) * (Z) * Math.exp(-Z*Z/2);

- } else {

- // jakarta commons implementation is acceptable

- return 0.5 * (1+Erf.erf(Z*SQRT_ONE_HALF));

- }

- } catch (MathException e) {

- throw new RuntimeException(e);

- }

- }

-

- public static double logitCumulativeNormalDistribution(double mean,

- double stdDev, double x) {

- try {

- double Z = (x - mean)/stdDev;

- double aZ = Math.abs(Z);

- if (aZ >= 1e9) {

- return Math.signum(Z)/2*Z*Z;

- } else if (aZ >= 2e4) {

- return Math.signum(Z)*(Z*Z/2-Math.log(SQRT_ONE_OVER_EIGHT_PI * 2/aZ));

- } else if (aZ >= 1.15) {

- double clogp = Z*Z/2-Math.log(SQRT_ONE_OVER_EIGHT_PI * normalTailCF(Z*Z/2, DOUBLE_EPS/aZ) * aZ);

- return Math.signum(Z)*(clogp+Math.log1p(-Math.exp(-clogp)));

- } else {

- double e = Erf.erf(Z*SQRT_ONE_HALF);

- return Math.log1p(e)-Math.log1p(-e);

- }

- } catch (MathException e) {

- throw new RuntimeException(e);

- }

- }

-

- public static double linterp(double x0, double x1, double y0, double y1, double x) {

- if (x0==x1) { return (y0+y1)/2; }

- if (Math.abs(x1)>Math.abs(x0)) {

- if (Math.abs(x) <= Math.abs(x0)) {

- double b = (x0/(x0-x1)) * (y1-y0) + y0;

- return (x/x1) * (y1-b) + b;

- } else {

- return ((x-x0)/(x1-x0)) * (y1-y0) + y0;

- }

- } else {

- if (Math.abs(x) <= Math.abs(x1)) {

- double b = (x1/(x1-x0)) * (y0-y1) + y1;

- return (x/x0) * (y0-b) + b;

- } else {

- return ((x-x1)/(x0-x1)) * (y0-y1) + y1;

- }

- }

- }

-

- public static double linterp(double[] x, double[] y, double nx) {

- if (y.length <= 1) { return y[0]; } // ArrayIndexOutOfBoundsException if y.length==0

- if (nx == Double.POSITIVE_INFINITY) {

- double yu = y[y.length-1], ypu = y[y.length-2];

- if (yu>ypu) {

- return Double.POSITIVE_INFINITY;

- } else if (yu<ypu) {

- return Double.NEGATIVE_INFINITY;

- } else {

- return yu;

- }

- }

- if (nx == Double.NEGATIVE_INFINITY) {

- double yu = y[0], ypu = y[1];

- if (yu>ypu) {

- return Double.POSITIVE_INFINITY;

- } else if (yu<ypu) {

- return Double.NEGATIVE_INFINITY;

- } else {

- return yu;

- }

- }

- int n = java.util.Arrays.binarySearch(x, nx);

- if (n >= 0) { return y[n]; }

- else if (n == -1) { n = -2; }

- else if (n == -x.length-1) { n = -x.length; }

- n = -n-1;

- return linterp(x[n-1], x[n], y[n-1], y[n], nx);

- }

-

- public static double[] linterp(double[] x, double[] y, double[] nx) {

- double[] ny = new double[nx.length];

- for (int i=0; i<nx.length; ++i) {

- ny[i] = linterp(x, y, nx[i]);

- }

- return ny;

- }

-

- public static double normalTailCF(double t, double absTol) {

- /* Disclaimer: I am not a numerical analyst */

- double p,q,r,s,pn,qn,rn,sn,b,f,fn;

- b = 0;

- pn = 0; qn = 1; rn = 1; sn = t;

- fn = 1/t;

- for (int k=1; k<400; k=k+2) {

- p = pn; q = qn; r = rn; s = sn; f = fn;

- /* continued fraction */

- pn = (k)*p + (q+q);

- qn = (k)*t*p + ((k+1)+(t+t))*q;

- rn = (k)*r + (s+s);

- sn = (k)*t*r + ((k+1)+(t+t))*s;

- // dirty trick, b=0 might go faster

- b = ((k+1)*(b+t))/((k)+(b+b)+(t+t));

- fn = (pn*b+qn)/(rn*b+sn);

- if (Math.abs(fn-f)<=absTol) {

- /* eh */

- break;

- }

- }

-// org.singinst.uf.common.LogUtil.info(String.format("Z = %20.16g, fn = %20.16g", Z, fn));

- return fn;

- }

-

- public static double inverseCumulativeProbability(double mean,

- double stdDev, double probability) {

- try {

- return new NormalDistributionImpl(mean, stdDev).inverseCumulativeProbability(probability);

- } catch (MathException e) {

- throw new RuntimeException(e);

- }

- }

-

- static final double EXPM1MX_IS_1_OVER_64_NEG = -0.18214213554545234604d;

- static final double EXPM1MX_IS_1_OVER_64_POS = 0.17171823868001516350d;

- public static double expm1mx(double x) {

- if (x>EXPM1MX_IS_1_OVER_64_POS || x<EXPM1MX_IS_1_OVER_64_NEG) {

- return Math.expm1(x)-x;

- } else {

- return ((((((((((1/39916800d)*x+1/3628800d)*x+1/362880d)*x+1/40320d)*x+1/5040d)*x+1/720d)*x+1/120d)*x+1/24d)*x+1/6d)*x+1/2d)*x*x;

- }

- }

- public static double expm1mxox(double x) {

- if (x>EXPM1MX_IS_1_OVER_64_POS || x<EXPM1MX_IS_1_OVER_64_NEG) {

- return (Math.expm1(x)-x)/x;

- } else {

- return ((((((((((1/39916800d)*x+1/3628800d)*x+1/362880d)*x+1/40320d)*x+1/5040d)*x+1/720d)*x+1/120d)*x+1/24d)*x+1/6d)*x+1/2d)*x;

- }

- }

- public static double exprel_2(double x) {

- if (x>EXPM1MX_IS_1_OVER_64_POS || x<EXPM1MX_IS_1_OVER_64_NEG) {

- return (Math.expm1(x)-x)/x/x;

- } else {

- return ((((((((((1/39916800d)*x+1/3628800d)*x+1/362880d)*x+1/40320d)*x+1/5040d)*x+1/720d)*x+1/120d)*x+1/24d)*x+1/6d)*x+1/2d);

- }

- }

- public static double exprel(double x) {

- if (x!=0) {

- return Math.expm1(x)/x;

- }

- else {

- return 1;

- }

- }

-

-

- static final double INTEGRAL_EXP_INTEGRAL_EXP_2O_LIM = 2.7878174747222281e-11d/24;

- public static double rpn_b_expm1_a_t_a_expm1_b_t_m_a_b_p_3_d_exp_d_a_d_b_d_a_b_m_d(double a, double b) {

- //return (b*Math.expm1(a)-a*Math.expm1(b))/Math.exp((a+b)/3)/a/b/(a-b);

- double s = (a*a + b*b - a*b)*(1/3d);

-

- if (s<INTEGRAL_EXP_INTEGRAL_EXP_2O_LIM) {

- return 0.5+s*(1/24d);

- }

-

- if (s>24) {

- double amb = a-b;

- if (Math.abs(amb)>Math.abs(a) && Math.abs(amb)>Math.abs(a)) {

- return Math.exp((-1/3d)*(a+b))*(expm1mxox(a)-expm1mxox(b))/(amb);

- } else if (Math.abs(a)>Math.abs(b)) {

- return Math.exp((1/3d)*(-amb+b))*(expm1mxox(amb)-expm1mxox(-b))/a;

- } else {

- return Math.exp((1/3d)*(amb+a))*(expm1mxox(-amb)-expm1mxox(-a))/b;

- }

- }

-

- double t = ((a-b+a)*(b-a+b)*(a+b))*(-1/27d);

-

- double r2 = s; double r1 = t; double r0 = s*s;

- double d = 720;

- double c = r0/d;

- boolean r0sm = false, rnsm = false;

- for (int i=7; i<60; ++i) {

- double rn = t*r2 + s*r1;

- r2 = r1; r1 = r0; r0 = rn; d = d*i;

- rn = rn/d;

- c = c + rn;

- r0sm = rnsm; rnsm = Math.abs(rn)<DOUBLE_EPS;

- if (r0sm && rnsm) { break; }

- }

- return ((c + t/120) + s/24) + 1/2d;

-

- }

-

- public static final double log_plus(double a, double b) {

- if (a<b) return b+Math.log1p(Math.exp(a-b));

- else return a+Math.log1p(Math.exp(b-a));

- }

-

- public static final double round(double value, int decimalPlaces) {

- if (!Double.isInfinite(value) & !Double.isNaN(value)) {

- return new BigDecimal(value).setScale(decimalPlaces, RoundingMode.HALF_EVEN).doubleValue();

- } else {

- return value;

- }

- }

-

- public static double otherExtreme(double extreme, double average) {

- return average * 2 - extreme;

- }

-

-// public static double[][] clogCompetingEvents(double[] t, double[][] clog_p ) {

-//

-// }

-

- public static double interpolate(SimplePoint p1,

- SimplePoint p2, double x) {

- double slope = (p2.y - p1.y) / (p2.x - p1.x);

- return p1.y + slope * (x - p1.x);

- }

-

- public static double bound(double value, double lowest, double highest) {

- if (value < lowest || Double.isNaN(value)) {

- return lowest;

- } else if (value > highest) {

- return highest;

- } else {

- return value;

- }

- }

-

- public static double average(double... doubleArray) {

- double total = 0;

- for (double d : doubleArray) {

- total += d;

- }

- return total / doubleArray.length;

- }

-

- static double DOUBLE_SINH_T_EQUALS_T_THRESHOLD = Math.sqrt(3*DOUBLE_EPS);

- static double DOUBLE_NEG_HALF_LOG_EPSILON = -Math.log(DOUBLE_EPS)/2;

- public static double exponentialIntervalAverage (double k0, double k1) {

- double t = Math.abs((k0-k1)/2);

- if (t<DOUBLE_SINH_T_EQUALS_T_THRESHOLD) {

- return Math.exp((k0+k1)/2);

- } else if (t>=DOUBLE_NEG_HALF_LOG_EPSILON) {

- return Math.exp(Math.max(k0, k1))/(t*2);

- } else {

- return Math.exp((k0+k1)/2)*(Math.sinh(t)/t);

- }

- }

- public static double exponentialIntervalIntegral (double k0, double k1, double width) {

- return width*exponentialIntervalAverage(k0,k1);

- }

-

- static double LOG_DOUBLE_MAX = Math.log(Double.MAX_VALUE);

- static double DOUBLE_MIN_NORMAL = 2.2250738585072014E-308; // = MathUtil.DOUBLE_MIN_NORMAL;

- static double LOG_DOUBLE_MIN_NORMAL = Math.log(DOUBLE_MIN_NORMAL);

- static double LOG_TWO = Math.log(2.0d);

- public static double weightedClogMix(double w0, double w1, double clog_p0, double clog_p1) {

- // disaster zone

- double clog_p;

- if(false) {

- if (clog_p0 < clog_p1) {

- clog_p = -Math.log((w0+w1*Math.exp(clog_p0-clog_p1))/(w0+w1))+clog_p0;

- if ((clog_p-clog_p0)<LOG_TWO) {

- clog_p = -Math.log1p((w0+w1*Math.expm1(clog_p0-clog_p1))/(w0+w1))+clog_p0;

- clog_p -= 100;

- }

- } else {

- clog_p = -Math.log((w0*Math.exp(clog_p1-clog_p0)+w1)/(w0+w1))+clog_p1;

- if ((clog_p-clog_p1)<LOG_TWO) {

- clog_p = -Math.log1p((w0*Math.expm1(clog_p1-clog_p0)+w1)/(w0+w1))+clog_p1;

- clog_p -= 100;

- }

- clog_p -= 200;

- }

-

- clog_p = clog((w0*expc(clog_p0)+w1*expc(clog_p1))/(w0+w1));

- clog_p = -Math.log1p(-(w0*-Math.expm1(-clog_p0)+w1*-Math.expm1(-clog_p1))/(w0+w1));

- }

-

- clog_p = -Math.log1p((w0*Math.expm1(-clog_p0)+w1*Math.expm1(-clog_p1))/(w0+w1));

- //double w0_times_expm1_neg_clog_p0 = w0*Math.expm1(-clog_p0);

- //double w1_times_expm1_neg_clog_p1 = w1*Math.expm1(-clog_p1);

- // clog_p = -Math.log1p((w0_times_expm1_neg_clog_p0+w1_times_expm1_neg_clog_p1)/(w0+w1));

- if (clog_p < LOG_TWO) {

- return clog_p;

- } else if (w0 == 0 && w1 != 0) {

- return clog_p1;

- } else if (w0 != 0 && w1 == 0) {

- return clog_p0;

- }

- double w0_times_exp_neg_clog_p0 = w0*Math.exp(-clog_p0);

- double w1_times_exp_neg_clog_p1 = w1*Math.exp(-clog_p1);

- double clog_p_by_log_exp = -Math.log((w0*Math.exp(-clog_p0)+w1*Math.exp(-clog_p1))/(w0+w1));

- if (w0_times_exp_neg_clog_p0 >= MathUtil.DOUBLE_MIN_NORMAL && w1_times_exp_neg_clog_p1 >= MathUtil.DOUBLE_MIN_NORMAL && clog_p_by_log_exp > Double.NEGATIVE_INFINITY && clog_p_by_log_exp < Double.POSITIVE_INFINITY) {

- return clog_p_by_log_exp;

- } else if /*(w1/w0 == 0 || w0/w1 == 0)*/ (true) {

- double l_w0 = Math.log(w0);

- double l_w1 = Math.log(w1);

-

- double m0 = -clog_p0 + l_w0;

- double m1 = -clog_p1 + l_w1;

-

- return log_plus(l_w0,l_w1)-log_plus(m0,m1);

-

- } else

-

- /* if (

- (clog_p0-clog_p1) < LOG_DOUBLE_MAX

- &&(clog_p0-clog_p1) > LOG_DOUBLE_MINNORMAL

- &&(clog_p0-clog_p1-Math.log(w0/w1)) < LOG_DOUBLE_MAX

- &&(clog_p0-clog_p1-Math.log(w0/w1)) > 0

- ) {

- } else */ {

-

- if (true) {

- //double discrim = clog_p0 + Math.log(Math.abs(w0)) - clog_p1 - Math.log(Math.abs(w1));

- //double discrim = -clog_p0 + Math.log(Math.abs(w0)) + clog_p1 - Math.log(Math.abs(w1));

- double discrim = Math.abs(clog_p0) + Math.abs(clog_p0-clog_p1-Math.log1p(Math.abs(w0/w1)))

- - Math.abs(clog_p1) - Math.abs(clog_p1-clog_p0-Math.log1p(Math.abs(w0/w1)));

- double center0_err =

- Math.abs(clog_p0)

- + Math.abs(1/(1+(Math.expm1(clog_p0-clog_p1))/(w0/w1+1)))

- * ( Math.abs(1/(w0/w1+1)) *

- ( Math.abs(clog_p0-clog_p1)

- + Math.abs(clog_p0)

- + Math.abs(clog_p1) )

- + Math.abs(Math.expm1(clog_p0-clog_p1)/(w0/w1+1)/(w0/w1+1) ) *

- ( Math.abs(1) ) );

- if (discrim < 0) {

- clog_p = clog_p0-Math.log1p((Math.expm1(clog_p0-clog_p1))/(w0/w1+1));

- //clog_p = clog_p0-Math.log1p((w1*Math.expm1(clog_p0-clog_p1))/(w0+w1));

- } else {

- clog_p = clog_p1-Math.log1p((Math.expm1(clog_p1-clog_p0))/(1+w1/w0));

- //clog_p = clog_p1-Math.log1p((w0*Math.expm1(clog_p1-clog_p0))/(w0+w1));

- }

- }

- }

-

- return clog_p;

- }

- public static double[] weightedClogMix(double w0, double w1, double[] clog_p0, double[] clog_p1) {

- double[] clog_p = new double[clog_p1.length];

- for (int i=0; i<clog_p.length; ++i)

- clog_p[i] = weightedClogMix(w0, w1, clog_p0[i], clog_p1[i]);

- return clog_p;

- }

-

- static double LOG_THREE = Math.log(3d);

- public static double weightedLogitMix(double w0, double w1, double logit_p0, double logit_p1) {

- double w = w0+w1;

- if ( !(w<0) && !(w>0) ) { return 0; }

- if (logit_p0 <= -LOG_THREE && logit_p1 <= -LOG_THREE) {

- return weightedLogitMixII(w0, w1, logit_p0, logit_p1);

- } else if (logit_p0 >= LOG_THREE && logit_p1 >= LOG_THREE) {

- return -weightedLogitMixII(w0, w1, -logit_p0, -logit_p1);

- }

- double p0z, p1z;

- if (logit_p0 >= 0) {

- p0z = -Math.expm1(-logit_p0)/(1+Math.exp(-logit_p0));

- } else {

- p0z = Math.expm1(logit_p0)/(1+Math.exp(logit_p0));

- }

- if (logit_p1 >= 0) {

- p1z = -Math.expm1(-logit_p1)/(1+Math.exp(-logit_p1));

- } else {

- p1z = Math.expm1(logit_p1)/(1+Math.exp(logit_p1));

- }

- double pz = (w0*p0z+w1*p1z)/w;

- if (Math.abs(pz)<=0.5) {

- return Math.log1p(pz)-Math.log1p(-pz);

- }

- if (pz<0) {

- return weightedLogitMixII(w0, w1, logit_p0, logit_p1);

- } else {

- return -weightedLogitMixII(w0, w1, -logit_p0, -logit_p1);

- }

- }

- static double weightedLogitMixII(double w0, double w1, double logit_p0, double logit_p1) {

- double lp;

- double lp0 = logit_p0-Math.log1p(Math.exp(logit_p0));

- double lp1 = logit_p1-Math.log1p(Math.exp(logit_p1));

- if (Math.abs(w0)<=Math.abs(w1)) {

- if (lp0 <= lp1) {

- lp = lp1 + Math.log1p((Math.expm1(lp0-lp1)*w0/w1)/(1+w0/w1));

- } else {

- lp = lp0 + Math.log1p(Math.expm1(lp1-lp0)/(1+w0/w1));

- }

- } else {

- if (lp0 <= lp1) {

- lp = lp1 + Math.log1p(Math.expm1(lp0-lp1)/(1+w1/w0));

- } else {

- lp = lp0 + Math.log1p((Math.expm1(lp1-lp0)*w1/w0)/(1+w1/w0));

- }

- }

- return lp-Math.log(-Math.expm1(lp));

- /*

- if (logit_p0 >= 0 && logit_p1 >= 0) {

- double t0 = (Math.exp(logit_p1)+1)*w0;

- double t1 = (Math.exp(logit_p0)+1)*w1;

- o = Math.log1p((t0*Math.expm1(logit_p0)+t1*Math.expm1(logit_p1))/(t0+t1));

- if (o > LOG_THREE) {

- o = -Math.log((t0*Math.exp(-logit_p0)+t1*Math.exp(-logit_p1))/(t0+t1));

- }

- return o;

- } else if (logit_p0 < 0 && logit_p1 < 0) {

- double t0 = (Math.exp(-logit_p1)+1)*w0;

- double t1 = (Math.exp(-logit_p0)+1)*w1;

- o = -Math.log1p((t0*Math.expm1(-logit_p0)+t1*Math.expm1(-logit_p1))/(t0+t1));

- if (o < -LOG_THREE) {

- o = Math.log((t0*Math.exp(logit_p0)+t1*Math.exp(logit_p1))/(t0+t1));

- }

- }

- double t0 = (Math.exp(logit_p1)+1)*w0;

- double t1 = (Math.exp(logit_p0)+1)*w1;

- return Math.log((t0*Math.exp(logit_p0)+t1*Math.exp(logit_p1))/(t0+t1));

- */

- }

-

-

- public static double clogIntervalAverage(double clog_p0, double clog_p1) {

- double d = Math.abs(clog_p0-clog_p1);

- double b = Math.min(clog_p0,clog_p1);

- if (d > 0.25) {

- return b-Math.log(-Math.expm1(-d)/d);

- } else {

- double dd = d*d;

- return (clog_p0+clog_p1)/2+(-1/24d)*dd*(1+(-1/120d)*dd*(1+(-1/63d)*dd*(1+(-3/160d)*dd*(1+(-2/99d)*dd))));

- }

- }

-

- /**

- *

- * @param t0 (assume cumulative probability of event is 0 at t0)

- * @param t1

- * @param mlr0 mean log hazard rate at t0

- * @param mlr1 mean log hazard rate at t1

- * @param sdlr0 standard deviation log hazard rate at t0

- * @param sdlr1 standard deviation log hazard rate at t1 (can have opposite sign to sdlr0)

- * @param t

- * @return array of complementary log probabilities of event by times t

- */

- public static double[] clogMarginalOfExponentiallyVaryingHazardModel (

- double t0, double t1,

- double mlr0, double mlr1,

- double sdlr0, double sdlr1,

- double[] t) {

- double[] clog_p = new double[t.length];

- for (int i=0; i<t.length; ++i) {

- double mlr_i = linterp(t0, t1, mlr0, mlr1, t[i]);

- double sdlr_i = linterp(t0, t1, sdlr0, sdlr1, t[i]);

- double clog_p_i = 0;

- double wt = 0;

- int j = -100;

- double z_l;

- double z_r = j/10d;

- double clog_d_l;

- double clog_d_r = exponentialIntervalIntegral(mlr0+z_r*sdlr0, mlr_i+z_r*sdlr_i, t[i]-t0);

- for (++j; j<100; ++j) {

- z_l = z_r;

- z_r = j/10d;

- clog_d_l = clog_d_r;

- clog_d_r = exponentialIntervalIntegral(mlr0+z_r*sdlr0, mlr_i+z_r*sdlr_i, t[i]-t0);

- double wt_z = exponentialIntervalIntegral(-z_l*z_l/2, -z_r*z_r/2, 1/10d);

- clog_p_i = weightedClogMix(wt, wt_z, clog_p_i, clogIntervalAverage(clog_d_l,clog_d_r));

- wt += wt_z;

- }

- clog_p[i] = Math.max(clog_p_i, 0);

- }

- return clog_p;

- }

-

- /**

- * a one-dimensional family of 5-dimensional weighted integrals of a 5-dimensional step function...

- * @param m

- * @param scienceEvents

- * @param rescheduledYears

- * @param iters

- * @return rescheduled_science_events

- */

- public static EventDiscreteDistributionSchedule clogMarginalScienceSpeedEventRescheduling(

- final ScienceSpeedModelParameters m,

- final EventDiscreteDistributionSchedule scienceEvents,

- final double[] rescheduledYears,

- int iters

- ) {

- int n = rescheduledYears.length;

- int e = scienceEvents.logitSubevents.length;

- EventDiscreteDistributionSchedule retVal = new EventDiscreteDistributionSchedule();

- retVal.time = rescheduledYears;

- retVal.clogProbEvent = new double[n];

- retVal.logitSubevents = new double[e][];

-

- // Monte Carlo

- double sequencedata_year_Z=0, sequencedata_factor_Z=0, population_year_Z=0, population_rate_Z=0, institutional_factor_Z=0;

- double dt0_seq, m_seq, b_seq;

- double dt0_pop, m_pop, b_pop;

- double m_inst;

- double dt, science_years=0;

- EventDiscreteDistribution ej;

- int i, j, k;

- double tn;

- for (i=0; i<iters; ++i) {

- if (i%2==0) {

- sequencedata_year_Z = RANDOM.nextGaussian();

- sequencedata_factor_Z = RANDOM.nextGaussian();

- population_year_Z = RANDOM.nextGaussian();

- population_rate_Z = RANDOM.nextGaussian();

- institutional_factor_Z = RANDOM.nextGaussian();

- } else {

- sequencedata_year_Z = -sequencedata_year_Z;

- sequencedata_factor_Z = -sequencedata_factor_Z;

- population_year_Z = -population_year_Z;

- population_rate_Z = -population_rate_Z;

- institutional_factor_Z = -institutional_factor_Z;

- }

- //sequencedata_year_Z = MathUtil.inverseCumulativeProbability(0, 1, (i+0.5d)/iters);

- dt0_seq = m.sequencedata_base_year + Math.exp(m.sequencedata_mean_log_year + sequencedata_year_Z * m.sequencedata_stddev_log_year) - m.institutional_base_year;

- m_seq = m.sequencedata_mean_slope_log_factor + sequencedata_factor_Z * m.sequencedata_stddev_slope_log_factor;

- b_seq = m.sequencedata_mean_init_log_factor + sequencedata_factor_Z * m.sequencedata_stddev_init_log_factor;

- dt0_pop = m.population_base_year + Math.exp(m.population_mean_log_year + population_year_Z * m.population_stddev_log_year) - m.institutional_base_year;

- m_pop = m.population_mean_slope_log_rate + population_rate_Z * m.population_stddev_slope_log_rate;

- b_pop = m.population_mean_init_log_rate + population_rate_Z * m.population_stddev_init_log_rate;

- m_inst = m.institutional_mean_slope_log_factor + institutional_factor_Z * m.institutional_stddev_slope_log_factor;

- for (j=0; j<n; ++j) {

- dt = rescheduledYears[j] - m.institutional_base_year;

- science_years = exprel( m_inst * dt ) * dt;

- if (dt>dt0_pop)

- science_years += (dt-dt0_pop)*(dt-dt0_pop) * Math.exp( m_inst*((1*dt0_pop+2*dt)/3/* - 0 */) + m_seq*((2*dt0_pop+1*dt)/3-dt0_seq)+b_seq + m_pop*((2*dt0_pop+1*dt)/3-dt0_pop)+b_pop ) * rpn_b_expm1_a_t_a_expm1_b_t_m_a_b_p_3_d_exp_d_a_d_b_d_a_b_m_d( -m_inst*(dt-dt0_pop), (m_pop+m_seq)*(dt-dt0_pop) );

- //science_years += (dt-dt0_pop)*(dt-dt0_pop) * Math.exp( m_inst*((1*dt0_pop+2*dt)/3/* - 0 */) + m_seq*((2*dt0_pop+1*dt)/3-dt0_seq)+b_seq + m_pop*((2*dt0_pop+1*dt)/3-dt0_pop)+b_pop ) * -threecancel( -(m_inst)*(t-t0_pop), (m_pop+m_seq)*(t-t0_pop) );

- //science_years += (dt-dt0_pop)*(dt-dt0_pop) * Math.exp( m_inst*((1*dt0_pop+2*dt)/3/* - 0 */) + (m_seq+m_pop)*((1*dt0_pop+2*dt)/3-dt0_seq)+b_seq+b_pop ) * rpn_b_expm1_a_t_a_expm1_b_t_m_a_b_p_3_d_exp_d_a_d_b_d_a_b_m_d( -(m_seq+m_inst)*(dt-dt0_pop), m_pop*(dt-dt0_pop) ) ;

- //science_years = org.singinst.uf.math.MathUtilTest.projectScienceYearsAnalytic(org.singinst.uf.math.MathUtilTest.applyScienceSpeedZValues(m, new ScienceSpeedZValues()), new double[] {dt})[0];

- ej = scienceEvents.logitnerp(science_years + m.institutional_base_year);

- ej = EventDiscreteDistribution.weightedMix(i/(i+1d), 1/(i+1d), retVal.get(j), ej);

- retVal.set(j, ej);

- }

- //org.singinst.uf.common.LogUtil.info(String.format("random %g leads to progress %g with dt0_seq=%+8.5f, m_seq=%+8.5f, b_seq=%+8.5f, dt0_pop=%+8.5f, m_pop=%+8.5f, b_pop=%+8.5f, m_inst=%+8.5f", dt0_pop, science_years, dt0_seq, m_seq, b_seq, dt0_pop, m_pop, b_pop, m_inst));

- }

-

- return retVal;

- }

-

- public static final Random RANDOM = new Random(1); /* not thread-safe? */

- public static final double NINETY_FIVE_PERCENTILE = 1.64485362695147;

-

- static public class ScienceSpeedModelParameters extends Object implements Cloneable {

- public double

- sequencedata_base_year,

- sequencedata_mean_log_year,

- sequencedata_stddev_log_year,

- sequencedata_mean_init_log_factor,

- sequencedata_stddev_init_log_factor,

- sequencedata_mean_slope_log_factor,

- sequencedata_stddev_slope_log_factor,

- population_base_year,

- population_mean_log_year,

- population_stddev_log_year,

- population_mean_init_log_rate,

- population_stddev_init_log_rate,

- population_mean_slope_log_rate,

- population_stddev_slope_log_rate,

- institutional_base_year,

- institutional_mean_slope_log_factor,

- institutional_stddev_slope_log_factor;

- public ScienceSpeedModelParameters clone() {

- try { return (ScienceSpeedModelParameters)(super.clone()); }

- catch (CloneNotSupportedException e) { throw new InternalError(e.getMessage()); }

- }

- }

-

- static public class EventDiscreteDistributionSchedule {

- public double[] time;

- public double[] clogProbEvent;

- public double[][] logitSubevents;

- public EventDiscreteDistribution get(int j) {

- EventDiscreteDistribution o = new EventDiscreteDistribution();

- o.clogProbEvent = clogProbEvent[j];

- o.logitSubevents = new double[logitSubevents.length];

- for (int i=0; i<logitSubevents.length; ++i) {

- o.logitSubevents[j] = logitSubevents[i][j];

- }

- return o;

- }

- public void set(int j, EventDiscreteDistribution e) {

- clogProbEvent[j] = e.clogProbEvent;

- for (int i=0; i<logitSubevents.length; ++i) {

- logitSubevents[i][j] = e.logitSubevents[j];

- }

- }

- public EventDiscreteDistribution logitnerp(double t) {

- EventDiscreteDistribution o = new EventDiscreteDistribution();

- o.clogProbEvent = MathUtil.linterp(time, clogProbEvent, t);

- o.logitSubevents = new double[logitSubevents.length];

- for (int i=0; i<logitSubevents.length; ++i) {

- o.logitSubevents[i] = MathUtil.linterp(time, logitSubevents[i], t);

- }

- return o;

- }

- public EventDiscreteDistributionSchedule logitnerp(double[] t) {

- EventDiscreteDistributionSchedule o = new EventDiscreteDistributionSchedule();

- o.clogProbEvent = MathUtil.linterp(time, clogProbEvent, t);

- o.logitSubevents = new double[logitSubevents.length][];

- for (int i=0; i<logitSubevents.length; ++i) {

- o.logitSubevents[i] = MathUtil.linterp(time, logitSubevents[i], t);

- }

- return o;

- }

- static public EventDiscreteDistributionSchedule weightedMix(double w0, double w1, EventDiscreteDistributionSchedule s0, EventDiscreteDistributionSchedule s1) {

- EventDiscreteDistributionSchedule o = new EventDiscreteDistributionSchedule();

- int j,i,m,n;

- n = s0.clogProbEvent.length;

- m = s0.logitSubevents.length;

- o.clogProbEvent = new double[n];

- o.logitSubevents = new double[m][];

- for (j=0; j<m; ++j)

- o.logitSubevents[j] = new double[n];

- for (i=0; i<n; ++i)

- o.set(i, EventDiscreteDistribution.weightedMix(w0,w1,s0.get(i),s1.get(i)) );

-

- return o;

- }

- }

-

- static public class ScienceSpeedScenario {

- public double

- sequencedata_year,

- sequencedata_init_log_factor,

- sequencedata_slope_log_factor,

- population_year,

- population_init_log_rate,

- population_slope_log_rate,

- institutional_base_year,

- institutional_slope_log_factor;

- }

-

- static public class ScienceSpeedScenarioReduced {

- public double

- institutional_slope_log_factor,

- sciencetalent_rel_year,

- sciencetalent_init_log_rate,

- sciencetalent_slope_log_rate;

- }

-

- static public class ScienceSpeedZValues {

- public double

- sequencedata_year_Z,

- sequencedata_factor_Z,

- population_year_Z,

- population_rate_Z,

- institutional_rate_Z;

- }

-

- static public class EventDiscreteDistribution {

- public double clogProbEvent;

- public double[] logitSubevents;

- public static EventDiscreteDistribution weightedMix(double w0, double w1, EventDiscreteDistribution e0, EventDiscreteDistribution e1) {

- EventDiscreteDistribution o = new EventDiscreteDistribution();

- o.clogProbEvent = MathUtil.weightedClogMix(w0, w1, e0.clogProbEvent, e1.clogProbEvent);

- o.logitSubevents = new double[e0.logitSubevents.length];

- double lw0, lw1;

- lw0 = Math.log(w0) + Math.log1p(-Math.exp(-e0.clogProbEvent));

- lw1 = Math.log(w1) + Math.log1p(-Math.exp(-e1.clogProbEvent));

- for (int i=0; i<e0.logitSubevents.length; ++i) {

- if (lw0>lw1) {

- lw1 = lw1 - lw0; lw0 = 0;

- } else {

- lw0 = lw0 - lw1; lw1 = 0;

- }

- o.logitSubevents[i] = MathUtil.weightedLogitMix(Math.exp(lw0), Math.exp(lw1), e0.logitSubevents[i], e1.logitSubevents[i]);

- lw0 = lw0 + MathUtil.log_logistic(-e0.logitSubevents[i]);

- lw1 = lw1 + MathUtil.log_logistic(-e1.logitSubevents[i]);

- }

- return o;

- }

- }

-

-

-}

-

-package org.singinst.uf.math;

-

-import junit.framework.TestCase;

-import junit.framework.Assert;

-

-import org.singinst.uf.math.MathUtil;

-//import org.singinst.uf.math.ScienceSpeedModelParameters;

-//import org.singinst.uf.math.ScienceSpeedZValues;

-//import org.singinst.uf.math.ScienceSpeedScenario;

-//import org.singinst.uf.math.ScienceSpeedScenarioReduced;

-//import org.singinst.uf.math.EventDiscreteDistribution;

-//import org.singinst.uf.math.EventDiscreteDistributionSchedule;

-import org.singinst.uf.common.LogUtil;

-import org.apache.commons.math.analysis.UnivariateRealFunction;

-import org.apache.commons.math.analysis.RombergIntegrator;

-

-public class MathUtilTest extends TestCase {

-

- static MathUtil.EventDiscreteDistributionSchedule heavisideSchedule(double t) {

- MathUtil.EventDiscreteDistributionSchedule o = new MathUtil.EventDiscreteDistributionSchedule();

- double[] o_dot_time = {0, t*(1-MathUtil.DOUBLE_EPS), t, 1e100};

- o.time = o_dot_time;

- double[] o_dot_clogProbEvent = {0,0,10,10};

- o.clogProbEvent = o_dot_clogProbEvent;

- o.logitSubevents = new double[0][];

- return o;

- }

-

- static MathUtil.ScienceSpeedModelParameters nullScienceModel() {

- MathUtil.ScienceSpeedModelParameters o = new MathUtil.ScienceSpeedModelParameters();

- o.sequencedata_base_year = 0;

- o.sequencedata_mean_log_year = 0;

- o.sequencedata_stddev_log_year = 0;

- o.sequencedata_mean_init_log_factor = 0;

- o.sequencedata_stddev_init_log_factor = 0;

- o.sequencedata_mean_slope_log_factor = 0;

- o.sequencedata_stddev_slope_log_factor = 0;

- o.population_base_year = 0;

- o.population_mean_log_year = 0;

- o.population_stddev_log_year = 0;

- o.population_mean_init_log_rate = 0;

- o.population_stddev_init_log_rate = 0;

- o.population_mean_slope_log_rate = 0;

- o.population_stddev_slope_log_rate = 0;

- o.institutional_base_year = 0;

- o.institutional_mean_slope_log_factor = 0;

- o.institutional_stddev_slope_log_factor = 0;

- return o;

- }

-

- static MathUtil.ScienceSpeedZValues nullScienceSpeedZValues () {

- MathUtil.ScienceSpeedZValues o = new MathUtil.ScienceSpeedZValues();

- return o;

- }

-

- static MathUtil.ScienceSpeedModelParameters pointMassScienceSpeedModel(MathUtil.ScienceSpeedModelParameters m, MathUtil.ScienceSpeedScenario s) {

- MathUtil.ScienceSpeedModelParameters o = nullScienceModel();

- o.institutional_mean_slope_log_factor = s.institutional_slope_log_factor;

- if (s.population_year > m.population_base_year) {

- o.population_mean_log_year = Math.log(s.population_year - m.population_base_year);

- o.population_base_year = m.population_base_year;

- } else {

- o.population_mean_log_year = Double.NEGATIVE_INFINITY;

- o.population_base_year = s.population_year;

- }

- o.population_mean_init_log_rate = s.population_init_log_rate;

- o.population_mean_slope_log_rate = s.population_slope_log_rate;

- if (s.sequencedata_year > m.sequencedata_base_year) {

- o.sequencedata_mean_log_year = Math.log(s.sequencedata_year - m.sequencedata_base_year);

- o.sequencedata_base_year = m.sequencedata_base_year;

- } else {

- o.sequencedata_mean_log_year = Double.NEGATIVE_INFINITY;

- o.sequencedata_base_year = s.sequencedata_year;

- }

- o.sequencedata_mean_init_log_factor = s.sequencedata_init_log_factor;

- o.sequencedata_mean_slope_log_factor = s.sequencedata_slope_log_factor;

- return o;

- }

-

- static MathUtil.ScienceSpeedScenario applyScienceSpeedZValues (MathUtil.ScienceSpeedModelParameters m, MathUtil.ScienceSpeedZValues z) {

- MathUtil.ScienceSpeedScenario o = new MathUtil.ScienceSpeedScenario();

- o.institutional_slope_log_factor =

- m.institutional_mean_slope_log_factor

- + z.institutional_rate_Z*m.institutional_stddev_slope_log_factor;

- o.population_year =

- m.population_base_year

- + Math.exp(

- m.population_mean_log_year

- + z.population_year_Z*m.population_stddev_log_year);

- o.sequencedata_year =

- m.sequencedata_base_year

- + Math.exp(

- m.sequencedata_mean_log_year

- + z.sequencedata_year_Z*m.sequencedata_stddev_log_year);

- o.population_init_log_rate =

- m.population_mean_init_log_rate

- + z.population_rate_Z*m.population_stddev_init_log_rate;

- o.population_slope_log_rate =

- m.population_mean_slope_log_rate

- + z.population_rate_Z*m.population_stddev_slope_log_rate;

- o.sequencedata_init_log_factor =

- + m.sequencedata_mean_init_log_factor

- + z.sequencedata_factor_Z*m.sequencedata_stddev_init_log_factor;

- o.sequencedata_slope_log_factor =

- + m.sequencedata_mean_slope_log_factor

- + z.sequencedata_factor_Z*m.sequencedata_stddev_slope_log_factor;

- return o;

- }

-

- static MathUtil.ScienceSpeedScenarioReduced applyScienceSpeedZValuesReduce (MathUtil.ScienceSpeedModelParameters m, MathUtil.ScienceSpeedZValues z) {

- MathUtil.ScienceSpeedScenarioReduced o = new MathUtil.ScienceSpeedScenarioReduced();

- o.institutional_slope_log_factor =

- m.institutional_mean_slope_log_factor

- + z.institutional_rate_Z*m.institutional_stddev_slope_log_factor;

- o.sciencetalent_rel_year =

- m.population_base_year

- + Math.exp(

- m.population_mean_log_year

- + z.population_year_Z*m.population_stddev_log_year)

- - m.institutional_base_year;

- double sequencedata_rel_year =

- m.sequencedata_base_year

- + Math.exp(

- m.sequencedata_mean_log_year

- + z.sequencedata_year_Z*m.sequencedata_stddev_log_year)

- - m.institutional_base_year;

- o.sciencetalent_init_log_rate =

- m.population_mean_init_log_rate

- + z.population_rate_Z*m.population_stddev_init_log_rate

- + m.sequencedata_mean_init_log_factor

- + (o.sciencetalent_rel_year - sequencedata_rel_year)

- * (m.sequencedata_mean_slope_log_factor)

- + z.sequencedata_factor_Z * (

- m.sequencedata_stddev_init_log_factor

- + (o.sciencetalent_rel_year - sequencedata_rel_year)

- * (m.sequencedata_stddev_slope_log_factor)

- );

- o.sciencetalent_slope_log_rate =

- m.population_mean_slope_log_rate

- + z.population_rate_Z*m.population_stddev_slope_log_rate

- + m.sequencedata_mean_slope_log_factor

- + z.sequencedata_factor_Z*m.sequencedata_stddev_slope_log_factor;

- return o;

- }

-

- static void logDoubleList(double[] l) {

- StringBuilder b = new StringBuilder();

- for (int i=0; i<l.length; ++i) {

- b.append(String.format("%20.16g ", l[i]));

- }

- org.singinst.uf.common.LogUtil.info(b.toString());

- }

-

- static void logDoubleList(String s, double[] l) {

- StringBuilder b = new StringBuilder(s);

- for (int i=0; i<l.length; ++i) {

- b.append(String.format("%20.16g ", l[i]));

- }

- org.singinst.uf.common.LogUtil.info(b.toString());

- }

-

- static double[] projectScienceYearsNumerical(MathUtil.ScienceSpeedScenario s, double[] t) throws org.apache.commons.math.FunctionEvaluationException,org.apache.commons.math.ConvergenceException {

- double[] o = new double[t.length];

- final double t0_seq, m_seq, b_seq;

- final double t0_pop, m_pop, b_pop;

- final double t0_inst, m_inst;

-

- t0_seq = s.sequencedata_year;

- m_seq = s.sequencedata_slope_log_factor;

- b_seq = s.sequencedata_init_log_factor;

- t0_pop = s.population_year;

- m_pop = s.population_slope_log_rate;

- b_pop = s.population_init_log_rate;

- t0_inst = s.institutional_base_year;

- m_inst = s.institutional_slope_log_factor;

-

- UnivariateRealFunction science_rate_after_pop = new UnivariateRealFunction() {

- public double value(double t) {

- return (t-t0_pop)*MathUtil.exprel((m_seq+m_pop)*(t-t0_pop))*Math.exp(m_inst*(t-t0_inst)+b_pop+m_seq*(t0_pop-t0_seq)+b_seq);

- //return (t-t0_pop)*MathUtil.exprel(m_pop*(t-t0_pop))*Math.exp(b_pop+m_inst*(t-t0_inst)+b_seq+m_seq*(t-t0_seq));

- }

- };

- double baseyear = s.institutional_base_year;

- double prevyear = t0_pop; double science_from_pop = 0;

- double year; double science;

- org.apache.commons.math.analysis.UnivariateRealIntegrator science_from_pop_integrator = new RombergIntegrator(science_rate_after_pop);

- science_from_pop_integrator.setRelativeAccuracy(32*MathUtil.DOUBLE_EPS);

- for (int i=0; i<t.length; ++i) {

- year = t[i];

- science = (year-baseyear)*MathUtil.exprel(m_inst*(year-baseyear));

- if (year >= t0_pop) {

- if (year > prevyear) {

- science_from_pop = science_from_pop + science_from_pop_integrator.integrate(prevyear, year);

- prevyear = year;

- } else if (year < prevyear) {

- science_from_pop = science_from_pop - science_from_pop_integrator.integrate(year, prevyear);

- prevyear = year;

- }

- science = science + science_from_pop;

- }

- o[i] = science + baseyear;

- }

- return o;

- }

-

- static double[] projectScienceYearsAnalytic(MathUtil.ScienceSpeedScenario s, double[] rescheduled_years) {

- double[] o = new double[rescheduled_years.length];

- double dt0_seq, m_seq, b_seq;

- double dt0_pop, m_pop, b_pop;

- double t0_inst, m_inst;

-

- double dt, science_years;

-

- t0_inst = s.institutional_base_year;

- m_inst = s.institutional_slope_log_factor;

- dt0_seq = s.sequencedata_year - t0_inst;

- m_seq = s.sequencedata_slope_log_factor;

- b_seq = s.sequencedata_init_log_factor;

- dt0_pop = s.population_year - t0_inst;

- m_pop = s.population_slope_log_rate;

- b_pop = s.population_init_log_rate;

-

- for (int j=0; j<rescheduled_years.length; ++j) {

- dt = rescheduled_years[j] - t0_inst;

- science_years = MathUtil.exprel( m_inst * dt ) * dt;

- if (dt > dt0_pop)

- science_years += (dt-dt0_pop)*(dt-dt0_pop) * Math.exp( m_inst*((1*dt0_pop+2*dt)/3/* - 0 */) + m_seq*((2*dt0_pop+1*dt)/3-dt0_seq)+b_seq + m_pop*((2*dt0_pop+1*dt)/3-dt0_pop)+b_pop ) * MathUtil.rpn_b_expm1_a_t_a_expm1_b_t_m_a_b_p_3_d_exp_d_a_d_b_d_a_b_m_d( -m_inst*(dt-dt0_pop), (m_pop+m_seq)*(dt-dt0_pop) ) ;

- o[j] = science_years + t0_inst;

- }

- return o;

- }

-

- static double[] projectScienceYearsAnalytic(MathUtil.ScienceSpeedScenarioReduced s, double[] reschedule_rel_years, double base_year) {

- double[] o = new double[reschedule_rel_years.length];

- double dt0_tal, m_tal, b_tal;

- double m_inst;

-

- double dt, science_years;

-

- m_inst = s.institutional_slope_log_factor;

- dt0_tal = s.sciencetalent_rel_year;

- m_tal = s.sciencetalent_slope_log_rate;

- b_tal = s.sciencetalent_init_log_rate;

-

- for (int j=0; j<reschedule_rel_years.length; ++j) {

- dt = reschedule_rel_years[j] - base_year;

- science_years = MathUtil.exprel( m_inst * dt ) * dt;

- if (dt > dt0_tal)

- science_years += (dt-dt0_tal)*(dt-dt0_tal) * Math.exp( m_inst*((1*dt0_tal+2*dt)/3/* - 0 */) + m_tal*((2*dt0_tal+1*dt)/3-dt0_tal)+b_tal ) * MathUtil.rpn_b_expm1_a_t_a_expm1_b_t_m_a_b_p_3_d_exp_d_a_d_b_d_a_b_m_d( -m_inst*(dt-dt0_tal), m_tal*(dt-dt0_tal) ) ;

- o[j] = science_years + base_year;

- }

- return o;

- }

-

- static public void testShowMeWhatClogIntervalLooksLike() throws Exception {

- double d = MathUtil.clogIntervalAverage(0, 4);

- org.singinst.uf.common.LogUtil.info(new Double(d).toString());

- }

-

- void notclearonthis() throws Exception {

- for (int i=0; i<2; ++i) {

- final double d = MathUtil.RANDOM.nextDouble();

- UnivariateRealFunction f = new UnivariateRealFunction() {

- public double value(double x) {

- return d*x;

- }

- };

- RombergIntegrator ri = new RombergIntegrator(f);

-

- org.singinst.uf.common.LogUtil.info(String.format("%g %g", d/2, ri.integrate(0, 1)));

- }

- }

-

- public void testMeh() throws Exception {

- {

-

- notclearonthis();

- notclearonthis();

-

- MathUtil.EventDiscreteDistributionSchedule s = new MathUtil.EventDiscreteDistributionSchedule();

- double[] s_dot_time = {0, 1, 2, 3, 4, 4.1, 4.2, 4.3, 4.4};

- double[] t = {-0.5,0,0.5,1,1.5,2,2.5,3,3.5,4,4.05,4.1,4.15,4.2,4.25,4.3,4.35,4.4,4.45};

-

- s.time = s_dot_time;

- double[] s_dot_clogProbEvent = {0,1,2,3,4,5,6,7,8};

- s.clogProbEvent = s_dot_clogProbEvent;

- s.logitSubevents = new double[0][];

- MathUtil.EventDiscreteDistributionSchedule r = s.logitnerp(t);

- StringBuilder b = new StringBuilder();

- for (int i=0; i<r.clogProbEvent.length; ++i) {

- b.append(r.clogProbEvent[i]).append(" ");

- }

- org.singinst.uf.common.LogUtil.info(b.toString());

- }

-

- {

- MathUtil.EventDiscreteDistributionSchedule s = heavisideSchedule(1);

- double[] t = {0,0.5,1,1.5,2};

- MathUtil.EventDiscreteDistributionSchedule r = s.logitnerp(t);

- logDoubleList(r.clogProbEvent);

- }

- }

-

- static double LOG_DOUBLE_EPS = Math.log(MathUtil.DOUBLE_EPS);

- static double square_clogged(double clog_p) {

- if (clog_p >= -LOG_DOUBLE_EPS) return clog_p - MathUtil.LOG_TWO;

- if (clog_p < LOG_DOUBLE_EPS) return 2*clog_p;

- return -Math.log(Math.exp(-clog_p)*(2-Math.exp(-clog_p)));

- }

- static double[] square_clogged(double[] clog_p) {

- double[] o = new double[clog_p.length];

- for (int i=0; i<clog_p.length; ++i) {

- if (clog_p[i] >= -LOG_DOUBLE_EPS) o[i] = clog_p[i] - MathUtil.LOG_TWO;

- if (clog_p[i] < LOG_DOUBLE_EPS) o[i] = 2*clog_p[i];

- o[i] = -Math.log(Math.exp(-clog_p[i])*(2-Math.exp(-clog_p[i])));

- }

- return o;

- }

-

- static double PHI = 0.618033988749894848d;

- static double COS_TWO_THIRDS_PI_PHI = 0.272883452047768708d;

- static double SIN_TWO_THIRDS_PI_PHI = 0.962047099469923622d;

-

- interface ScenarioSource { MathUtil.ScienceSpeedScenarioReduced scenario(int i); };

-

- public void testWhatever() throws Exception {

- int iters = 1000;

- MathUtil.ScienceSpeedModelParameters m = nullScienceModel();

- MathUtil.ScienceSpeedScenario sss = new MathUtil.ScienceSpeedScenario();

- MathUtil.ScienceSpeedScenarioReduced sssr = new MathUtil.ScienceSpeedScenarioReduced();

- MathUtil.ScienceSpeedZValues z = new MathUtil.ScienceSpeedZValues();

-

- sss.institutional_base_year = -MathUtil.RANDOM.nextDouble();

- //sss.institutional_base_year = -0.5;

- sss.institutional_slope_log_factor = MathUtil.RANDOM.nextGaussian();

- //sss.institutional_slope_log_factor = Math.log(1.0001)*10;

- sss.sequencedata_init_log_factor = MathUtil.RANDOM.nextGaussian();

- sss.sequencedata_slope_log_factor = MathUtil.RANDOM.nextGaussian();

- //sss.sequencedata_slope_log_factor = Math.log(1000);

- sss.sequencedata_year = MathUtil.RANDOM.nextDouble();

- sss.population_init_log_rate = MathUtil.RANDOM.nextGaussian();

- // //sss.population_init_log_rate = Math.log(10000);

- sss.population_slope_log_rate = MathUtil.RANDOM.nextGaussian();

- sss.population_year = MathUtil.RANDOM.nextDouble();

-

- MathUtil.EventDiscreteDistributionSchedule s = heavisideSchedule(1);

- m.institutional_base_year = sss.institutional_base_year;

- m.population_base_year = 0;

- m.population_mean_log_year = 0;

- m.population_stddev_log_year = 1;

- m.sequencedata_base_year = 0;

- m.sequencedata_mean_log_year = 0;

- m.sequencedata_stddev_log_year = 1;

- m = pointMassScienceSpeedModel(m, sss);

- //m.institutional_base_year = sss.institutional_base_year;

- sss = applyScienceSpeedZValues(m,z);

- sssr = applyScienceSpeedZValuesReduce(m,z);

- double[] t = {0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.3,1.4,1.5,2.0};

- double[] clog_weird

- = {0 ,0 ,1 ,1 ,2 ,2 ,3 ,3 ,4 ,4 ,5 ,5 ,6 ,6 ,7 ,7 ,8 };

-

- logDoubleList(t);

- logDoubleList(projectScienceYearsNumerical(sss,t));

- logDoubleList(projectScienceYearsAnalytic(sss,t));

- logDoubleList(projectScienceYearsAnalytic(sssr,t,m.institutional_base_year));

- s.time = t;

- s.clogProbEvent = t;

- //s.clogProbEvent = clog_weird;

- MathUtil.EventDiscreteDistributionSchedule r = MathUtil.clogMarginalScienceSpeedEventRescheduling(m, s, t, iters);

- logDoubleList(r.clogProbEvent);

-

- r = s;

- MathUtil.EventDiscreteDistributionSchedule r_regular = s;

- MathUtil.EventDiscreteDistributionSchedule r_montecarlo = s;

- MathUtil.ScienceSpeedModelParameters m_v = m.clone();

-

- double wt = 0, wt_i;

- double[] squared_clog = new double[t.length];

-

- m_v.institutional_stddev_slope_log_factor = 1;

- for (int i=0; i<=200; ++i) {

- z.institutional_rate_Z = (i-100)/10d;

- sssr = applyScienceSpeedZValuesReduce(m_v,z);

- wt_i = Math.exp(-z.institutional_rate_Z*z.institutional_rate_Z/2);

- double[] rescheduled_t = projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year);

- MathUtil.EventDiscreteDistributionSchedule s_i = s.logitnerp(rescheduled_t);

- r_regular = MathUtil.EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s_i);

- //r_regular = EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)));

- wt = wt + wt_i;

- }

- r_montecarlo = MathUtil.clogMarginalScienceSpeedEventRescheduling(m_v, s, t, iters);

- logDoubleList(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year));

- logDoubleList(s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)).clogProbEvent);

- logDoubleList("inst rate Z by regular", r_regular.clogProbEvent);

- logDoubleList("inst rate Z by regular", r_montecarlo.clogProbEvent);

- m_v.institutional_stddev_slope_log_factor = 0;

- z.institutional_rate_Z = 0;

-

- m_v.population_stddev_init_log_rate = 1;

- wt = 0;

- for (int i=0; i<=200; ++i) {

- z.population_rate_Z = (i-100)/10d;

- sssr = applyScienceSpeedZValuesReduce(m_v,z);

- wt_i = Math.exp(-z.population_rate_Z*z.population_rate_Z/2);

- double[] rescheduled_t = projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year);

- MathUtil.EventDiscreteDistributionSchedule s_i = s.logitnerp(rescheduled_t);

- r_regular = MathUtil.EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s_i);

- //r_regular = EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)));

- wt = wt + wt_i;

- }

- r_montecarlo = MathUtil.clogMarginalScienceSpeedEventRescheduling(m_v, s, t, iters);

- logDoubleList("pop init rate Z by regular", r_regular.clogProbEvent);

- logDoubleList("pop init rate Z by MC ", r_montecarlo.clogProbEvent);

- m_v.population_stddev_init_log_rate = 0;

- z.population_rate_Z = 0;

-

- m_v.population_stddev_slope_log_rate = 1;

- wt = 0;

- for (int i=0; i<=200; ++i) {

- z.population_rate_Z = (i-100)/10d;

- sssr = applyScienceSpeedZValuesReduce(m_v,z);

- wt_i = Math.exp(-z.population_rate_Z*z.population_rate_Z/2);

- double[] rescheduled_t = projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year);

- MathUtil.EventDiscreteDistributionSchedule s_i = s.logitnerp(rescheduled_t);

- r_regular = MathUtil.EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s_i);

- //r_regular = EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)));

- wt = wt + wt_i;

- }

- r_montecarlo = MathUtil.clogMarginalScienceSpeedEventRescheduling(m_v, s, t, iters);

- logDoubleList("pop slope rate Z by regular", r_regular.clogProbEvent);

- logDoubleList("pop slope rate Z by MC ", r_montecarlo.clogProbEvent);

- m_v.population_stddev_slope_log_rate = 0;

- z.population_rate_Z = 0;

-

- m_v.population_stddev_log_year = 1;

- wt = 0;

- for (int i=0; i<=200; ++i) {

- z.population_year_Z = (i-100)/10d;

- sssr = applyScienceSpeedZValuesReduce(m_v,z);

- wt_i = Math.exp(-z.population_year_Z*z.population_year_Z/2);

- double[] rescheduled_t = projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year);

- MathUtil.EventDiscreteDistributionSchedule s_i = s.logitnerp(rescheduled_t);

- r_regular = MathUtil.EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s_i);

- //r_regular = EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)));

- wt = wt + wt_i;

- }

- logDoubleList("pop year Z by regular Z", r_regular.clogProbEvent);

- for (int i=0; i<iters; ++i) {

- z.population_year_Z = MathUtil.inverseCumulativeProbability(0, 1, (i+0.5d)/iters);

- sssr = applyScienceSpeedZValuesReduce(m_v,z);

- wt_i = 1;

- double[] rescheduled_t = projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year);

- MathUtil.EventDiscreteDistributionSchedule s_i = s.logitnerp(rescheduled_t);

- r_regular = MathUtil.EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s_i);

- //r_regular = EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)));

- wt = wt + wt_i;

- //org.singinst.uf.common.LogUtil.info(String.format("regular %g leads to progress %g", sssr.sciencetalent_rel_year, rescheduled_t[rescheduled_t.length-1]));

- }

- logDoubleList("pop year Z by regular q", r_regular.clogProbEvent);

- r_montecarlo = MathUtil.clogMarginalScienceSpeedEventRescheduling(m_v, s, t, iters);

- logDoubleList("pop year Z by MC ", r_montecarlo.clogProbEvent);

- m_v.population_stddev_log_year = 0;

- z.population_year_Z = 0;

-

- m_v.sequencedata_stddev_init_log_factor = 1;

- wt = 0;

- for (int i=0; i<=200; ++i) {

- z.sequencedata_factor_Z = (i-100)/10d;

- sssr = applyScienceSpeedZValuesReduce(m_v,z);

- wt_i = Math.exp(-z.sequencedata_factor_Z*z.sequencedata_factor_Z/2);

- double[] rescheduled_t = projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year);

- MathUtil.EventDiscreteDistributionSchedule s_i = s.logitnerp(rescheduled_t);

- r_regular = MathUtil.EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s_i);

- //r_regular = EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)));

- wt = wt + wt_i;

- }

- r_montecarlo = MathUtil.clogMarginalScienceSpeedEventRescheduling(m_v, s, t, iters);

- logDoubleList("seqdata init factor Z by regular", r_regular.clogProbEvent);

- logDoubleList("seqdata init factor Z by MC ", r_montecarlo.clogProbEvent);

- m_v.sequencedata_stddev_init_log_factor = 0;

- z.sequencedata_factor_Z = 0;

-

- m_v.sequencedata_stddev_slope_log_factor = 1;

- wt = 0;

- for (int i=0; i<=200; ++i) {

- z.sequencedata_factor_Z = (i-100)/10d;

- sssr = applyScienceSpeedZValuesReduce(m_v,z);

- wt_i = Math.exp(-z.sequencedata_factor_Z*z.sequencedata_factor_Z/2);

- double[] rescheduled_t = projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year);

- MathUtil.EventDiscreteDistributionSchedule s_i = s.logitnerp(rescheduled_t);

- r_regular = MathUtil.EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s_i);

- //r_regular = EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)));

- wt = wt + wt_i;

- }

- r_montecarlo = MathUtil.clogMarginalScienceSpeedEventRescheduling(m_v, s, t, iters);

- logDoubleList("seqdata slope factor Z by regular", r_regular.clogProbEvent);

- logDoubleList("seqdata slope factor Z by MC ", r_montecarlo.clogProbEvent);

- m_v.sequencedata_stddev_slope_log_factor = 0;

- z.sequencedata_factor_Z = 0;

-

- m_v.sequencedata_stddev_log_year = 1;

- wt = 0;

- for (int i=0; i<=200; ++i) {

- z.sequencedata_year_Z = (i-100)/10d;

- sssr = applyScienceSpeedZValuesReduce(m_v,z);

- wt_i = Math.exp(-z.sequencedata_year_Z*z.sequencedata_year_Z/2);

- double[] rescheduled_t = projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year);

- MathUtil.EventDiscreteDistributionSchedule s_i = s.logitnerp(rescheduled_t);

- r_regular = MathUtil.EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s_i);

- //r_regular = EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)));

- wt = wt + wt_i;

- }

- r_montecarlo = MathUtil.clogMarginalScienceSpeedEventRescheduling(m_v, s, t, iters);

- logDoubleList("seqdata year Z by regular Z", r_regular.clogProbEvent);

- for (int i=0; i<iters; ++i) {

- z.sequencedata_year_Z = MathUtil.inverseCumulativeProbability(0, 1, (i+0.5d)/iters);

- sssr = applyScienceSpeedZValuesReduce(m_v,z);

- wt_i = 1;

- double[] rescheduled_t = projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year);

- MathUtil.EventDiscreteDistributionSchedule s_i = s.logitnerp(rescheduled_t);

- r_regular = MathUtil.EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s_i);

- //r_regular = EventDiscreteDistributionSchedule.weightedMix(wt, wt_i, r_regular, s.logitnerp(projectScienceYearsAnalytic(sssr,t,m_v.institutional_base_year)));

- wt = wt + wt_i;

- //org.singinst.uf.common.LogUtil.info(String.format("regular %g leads to progress %g", sssr.sciencetalent_rel_year, rescheduled_t[rescheduled_t.length-1]));

- }

- logDoubleList("seqdata year Z by regular q", r_regular.clogProbEvent);

- logDoubleList("seqdata year Z by MC ", r_montecarlo.clogProbEvent);

- m_v.sequencedata_stddev_log_year = 0;

- z.sequencedata_year_Z = 0;

-

-

- fail();

- }

-

- static double SQRT_EPS = Math.sqrt(MathUtil.DOUBLE_EPS);

- public double u(double x) {

- return x+SQRT_EPS*(Math.abs(x)+MathUtil.DOUBLE_MIN_NORMAL);

- }

- public double d(double x) {

- return x-SQRT_EPS*(Math.abs(x)/*+MathUtil.DOUBLE_MIN_NORMAL*/);

- }

-

- public void testWeightedClogMix() throws Exception {

- int i,j,k,l;

- double o, cl_p;

- for (i=0; i<6; ++i) for (j=0; j<6; ++j) for (k=0; k<6; ++k) for (l=0; l<6; ++l) {

- o = MathUtil.weightedClogMix(i, j, k, l);

- cl_p = MathUtil.clog((i*MathUtil.expc(k)+j*MathUtil.expc(l))/(i+j));

- Assert.assertEquals(cl_p, o, 1e-14d*cl_p);

- //LogUtil.info(String.format("%d %d %d %d %20.14g %20.14g %20.14g %20.14g", i,j,k,l, o, p, o-p, Math.exp(-((o-p)%100)) ));

- }

- double a,b,c,d;

- double w0, w1, clog_p0, clog_p1;

- double cl_p_hi, cl_p_lo;

- for (a=-50; a<=50; a+=25) for (b=-50; b<50; b+=25) for (c=0; c<=100; c+=25) for (d=0; d<=100; d+=25) {

- w0 = Math.exp(a); clog_p0 = c;

- w1 = Math.exp(b); clog_p1 = d;

- o = MathUtil.weightedClogMix(w0, w1, clog_p0, clog_p1);

-// cl_p = clog_p0-Math.log1p((w1*Math.expm1(clog_p0-clog_p1))/(w0+w1));

-// Assert.assertEquals(cl_p, o, 16*MathUtil.DOUBLE_EPS*(

-// Math.abs( clog_p0 )

-// + Math.abs( Math.log1p((w1*Math.expm1(clog_p0-clog_p1))/(w0+w1)) )

-// + Math.abs( 1/(1+(w1*Math.expm1(clog_p0-clog_p1))/(w0+w1)) )

-// ) );

- cl_p = clog_p0-Math.log1p((Math.expm1(clog_p0-clog_p1))/(w0/w1+1));

- cl_p_hi = u(u(clog_p0)-d(Math.log1p(Math.max(1,d(d(Math.expm1(d(d(clog_p0)-u(clog_p1))))/(w0/w1+1))))));

- cl_p_lo = d(d(clog_p0)-u(Math.log1p( u(u(Math.expm1(u(u(clog_p0)-d(clog_p1))))/(w0/w1+1)) )));

- try {

- Assert.assertEquals(cl_p, o, 16*SQRT_EPS*(Math.abs(cl_p_hi-cl_p)+Math.abs(cl_p_lo-cl_p)));

- } catch (junit.framework.AssertionFailedError e) {

- o = MathUtil.weightedClogMix(w0, w1, clog_p0, clog_p1);

- throw(e);

- }

-

-// cl_p = clog_p1-Math.log1p((w0*Math.expm1(clog_p1-clog_p0))/(w0+w1));

-// Assert.assertEquals(cl_p, o, 16*MathUtil.DOUBLE_EPS*(

-// Math.abs( clog_p1 )

-// + Math.abs( Math.log1p((w0*Math.expm1(clog_p1-clog_p0))/(w0+w1)) )

-// + Math.abs( 1/(1+(w0*Math.expm1(clog_p1-clog_p0))/(w0+w1)) )

-// ) );

- cl_p = clog_p1-Math.log1p((Math.expm1(clog_p1-clog_p0))/(w1/w0+1));

- cl_p_hi = u(u(clog_p1)-d(Math.log1p(Math.max(1,d(d(Math.expm1(d(d(clog_p1)-u(clog_p0))))/(w1/w0+1))))));

- cl_p_lo = d(d(clog_p1)-u(Math.log1p( u(u(Math.expm1(u(u(clog_p1)-d(clog_p0))))/(w1/w0+1)) )));

- try {

- Assert.assertEquals(cl_p, o, 16*SQRT_EPS*(Math.abs(cl_p_hi-cl_p)+Math.abs(cl_p_lo-cl_p)));

- } catch (junit.framework.AssertionFailedError e) {

- o = MathUtil.weightedClogMix(w0, w1, clog_p0, clog_p1);

- throw(e);

- }

-

- }

- }

-

- public void test() throws Exception {

- }

-}

-package org.singinst.uf.math;

-

-import java.util.Arrays;

-

-public class SimplePoint {

-

- public SimplePoint(double x, double y) {

- this.x = x;

- this.y = y;

- }

- public final double x;

- public final double y;

-

- @Override

- public boolean equals(Object obj) {

- if (obj instanceof SimplePoint) {

- SimplePoint other = (SimplePoint) obj;

- return other.x == x && other.y == y;

- } else {

- return false;

- }

- }

- @Override

- public int hashCode() {

- return canon().hashCode();

- }

- @Override

- public String toString() {

- return canon().toString();

- }

-

- private Object canon() {

- return Arrays.asList(x, y);

- }

-

-

-}

-package org.singinst.uf.model;

-

-import java.awt.Color;

-import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-

-

-

-public class AiRelation extends YearwiseCalculationRelation {

-

- public boolean displayHtml;

-

- public AiRelation(Node node) {

- super(node);

- this.displayHtml = displayHtml;

- }

-

- @Override

- protected Calculation getCalculation(double year) {

- return new MultiplicationCalculation("Probability of either sort of AI by year " + (int) year,

- probabilityAiIfAiIsPossible(year),

- probabilityAiIsPossible());

-

- }

-

- private Calculation probabilityAiIfAiIsPossible(final double year) {

- return new Calculation("Probability of AI (if AI is possible) by year " + (int) year) {

-

- @Override

- protected double rawEvaluate(StringBuilder htmlConsole) {

- return MathUtil.expc(

- MathUtil.clog(

- ScalarValueHolder.findById(NodeIDString.C1_5, ScalarSubIDString.yearProbabilityID(year)).evaluate(new StringBuilder())

- / probabilityAiIsPossible().evaluate()

- )

- + MathUtil.clog(getNonNeuromorphicAiCalculationForYear(year).evaluate())

- );

- }

-

- @Override

- public Color getColor() {

- return Color.BLUE;

- }

- };

-/* ScalarValueHolder.get(NodeIDString.C1_5, yearScalarName(year)),

- getNonNeuromorphicAiCalculationForYear(year)) {

- @Override

- protected double rawEvaluate(StringBuilder htmlConsole) {

- double probabilityOfNot = 1;

- for (Evaluable calculation : getCalculations()) {

- probabilityOfNot *= (1 - calculation.evaluate(htmlConsole));

- }

- htmlConsole.append(getDescription());

- return 1 - probabilityOfNot;

- }

- };*/

- }

- private Calculation getNonNeuromorphicAiCalculationForYear(final double year) {

- return new Calculation("Probability that non-neuromorphic AI will be created by year " + year, false) {

-

- @Override

- protected double rawEvaluate(StringBuilder htmlConsole) {

- double mean = ScalarValueHolder.findById(NodeIDString.Q1_6, ScalarSubIDString.MEAN).getValue();

- double stdDev = ScalarValueHolder.findById(NodeIDString.Q1_6, ScalarSubIDString.STD_DEV).getValue();

- return MathUtil.cumulativeNormalDistribution(mean, stdDev, Math.log10(year - ModelUtil.ANCHOR_FAR_YEAR));

- }

- };

- }

-}

-package org.singinst.uf.model;

-

-

-public class AtLeastOneHappensCalculation extends CompositeCalculation {

-

- public AtLeastOneHappensCalculation(String description,

- Evaluable... calculations) {

- super(description, calculations);

- }

-

- @Override

- protected double rawEvaluate(StringBuilder htmlConsole) {

- double probabilityOfNot = 1;

- for (Evaluable calculation : getCalculations()) {

- probabilityOfNot *= (1 - calculation.evaluate(htmlConsole));

- }

- htmlConsole.append(getDescription());

- return 1 - probabilityOfNot;

- }

-}

-package org.singinst.uf.model;

-

-import org.singinst.uf.presenter.LineBounded;

-import org.singinst.uf.presenter.LineBounds;

-

-public class Axis implements LineBounded {

-

- private final LineBounds lineBounds;

- private final boolean visibleAxis;

-

- public Axis(LineBounds lineBounds) {

- this(lineBounds, true);

- }

-

- public Axis(LineBounds lineBounds, boolean visibleAxis) {

- this.lineBounds = lineBounds;

- this.visibleAxis = visibleAxis;

- }

-

- public LineBounds getLineBounds() {

- return lineBounds;

- }

-

- public boolean isVisibleAxis() {

- return visibleAxis;

- }

-

-}

-package org.singinst.uf.model;

-

-import java.awt.Color;

-import org.singinst.uf.presenter.HtmlUtil;

-

-import org.singinst.uf.math.MathUtil;

-

-public abstract class Calculation implements Evaluable {

-

- private final String description;

- private StringBuilder htmlConsole = new StringBuilder();

- private final boolean prependDescription;

-

- public Calculation(String description) {

- this(description, true);

- }

-

- public Calculation(String description, boolean prependDescription) {

- this.description = description.intern();

- this.prependDescription = prependDescription;

- }

-

- public String getDescription() {

- return description;

- }

-

- public final double evaluate() {

- return evaluate(getHtmlConsole());

- }

-

- public final double evaluate(StringBuilder htmlConsole) {

- if (prependDescription) {

- htmlConsole.append(description);

- }

- double raw = rawEvaluate(htmlConsole);

- double rounded = MathUtil.round(raw, 12);

- htmlConsole.append(" = " + HtmlUtil.htmlcolorFromColor(getColor(), rounded) + "<br>\n");

- return rounded;

- }

-

- protected abstract double rawEvaluate(StringBuilder htmlConsole);

-

- /**

- * Returns the color for this calculation, so that we consistently display the correct one.

- *

- * @return

- * The color to display this calculation

- */

- public Color getColor() {

- return Color.BLACK;

- }

-

- public StringBuilder getHtmlConsole() {

- return htmlConsole;

- }

-

- public void setHtmlConsole(StringBuilder htmlConsole) {

- this.htmlConsole = htmlConsole;

- }

-

-}

-package org.singinst.uf.model;

-

-import java.awt.Color;

-import java.util.ArrayList;

-import java.util.Collections;

-import java.util.List;

-

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.presenter.Completion;

-import org.singinst.uf.presenter.ScalarValuePointList;

-import org.singinst.uf.presenter.LineBounds;

-

-public abstract class CalculationRelation extends ScalarRelation {

-

- private final Node node;

-

- private final List<ScalarSchema> scalarSchemata = new ArrayList<ScalarSchema>();

- private final List<ScalarValueHolder> scalarValueHolders = new ArrayList<ScalarValueHolder>();

- private final StringBuilder htmlConsole = new StringBuilder();

- private final ScalarValueDependency dependency = new ScalarValueDependency();

-

- private final ConclusionReportGenerator conclusion = new ConclusionReportGenerator() {

-

- public String getText(ScalarValueHolder scalarValueHolder, double value) {

- return htmlConsole.toString();

- }

-

- };

-

- private final ScalarValuePointList curveList = new ScalarValuePointList() {

-

- @Override

- public Runnable updater() {

- return new Runnable() {

-

- public void run() {

- Completion completion = Completion.getInstance();

- if (completion.init("Calculating " + node.getIdString(), ModelUtil.LATEST_YEAR - ModelUtil.EARLIEST_YEAR)) {

- for (int year = ModelUtil.EARLIEST_YEAR; year <= ModelUtil.LATEST_YEAR; year++) {

- ScalarValueHolder scalarValueHolder = ScalarValueHolder.findById(node.getIdString(), ScalarSubIDString.yearProbabilityID(year));

- scalarValueHolder.getValue();

- completion.tick();

- }

- }

- }

-

- };

- }

-

- };

-

- public CalculationRelation(Node node) {

- super(new Axis(new LineBounds(ModelUtil.EARLIEST_YEAR, ModelUtil.LATEST_YEAR)),

- new Axis(new LineBounds(0, 1)));

- this.node = node;

-

- List<Double> yearList = new ArrayList<Double>();

- for (double year = ModelUtil.EARLIEST_YEAR; year <= ModelUtil.LATEST_UNACCELERATED_YEAR; year += ModelUtil.YEAR_STEPSIZE) {

- yearList.add((double) year);

- }

- List<Calculation> calculations = getCalculations(yearList);

-

- for (double year = ModelUtil.EARLIEST_YEAR; year <= ModelUtil.LATEST_UNACCELERATED_YEAR; year += ModelUtil.YEAR_STEPSIZE) {

- ScalarSchema scalarSchema = new ScalarSchema(

- node, ScalarSubIDString.yearProbabilityID(year), new LineBounds(0, 1), "", "", "", null, true);

- scalarSchemata.add(scalarSchema);

- ScalarValueHolder scalarValueHolder = scalarSchema.getScalarValueHolder();

- if (year <= ModelUtil.LATEST_YEAR && ((year % 1d) == 0d) /* could use elementOf to ModelUtil.BASIC_MODEL_YEARS? */ ) {

- scalarValueHolders.add(scalarValueHolder);

- }

- scalarValueHolder.setCalculation(calculations.remove(0));

- }

- }

-

- protected abstract List<Calculation> getCalculations(List<Double> year);

-

- @Override

- public List<ScalarValuePointList> getPointLists() {

- List<SimplePoint> pointList = new ArrayList<SimplePoint>();

- ScalarValueHolder lastScalarValue = null;

- Boolean needsCalc = null;

- for (int year = ModelUtil.EARLIEST_YEAR; year <= ModelUtil.LATEST_YEAR; year++) {

- lastScalarValue = ScalarValueHolder.findById(node.getIdString(), ScalarSubIDString.yearProbabilityID(year));

- if (needsCalc == null) {

- if (lastScalarValue.needsCalc(new NeedsCalcCache())) {

- curveList.setHypothesisPoints(null);

- return Collections.singletonList(curveList);

- } else {

- needsCalc = false;

- }

- }

- double value = lastScalarValue.getValue();

-

- // TODO4

- // dependency.validate();

-

- pointList.add(new SimplePoint(year, value));

- }

- if (lastScalarValue != null) {

- StringBuilder newConsoleText = lastScalarValue.getCalculation().getHtmlConsole();;

- if (this instanceof AiRelation) {

- newConsoleText = new StringBuilder();

- lastScalarValue.getCalculation().setHtmlConsole( newConsoleText );

- }

-

- if (!htmlConsole.toString().equals(newConsoleText.toString())) {

- htmlConsole.setLength(0);

- htmlConsole.append(newConsoleText);

- lastScalarValue.notifyListeners();

- }

- }

- curveList.setHypothesisPoints(pointList);

- return Collections.singletonList(curveList);

- }

-

- public List<? extends ConclusionReportGenerator> getConclusionGenerators() {

- return Collections.singletonList(conclusion);

- }

-

- public List<ScalarValueHolder> getScalarValues() {

- return scalarValueHolders;

- }

-

- protected ScalarValueDependency getDependency() {

- return dependency;

- }

-

- protected Calculation probabilityAiIsPossible() {

- return new Calculation("Probability that AI is possible in principle") {

- @Override

- protected double rawEvaluate(StringBuilder htmlConsole) {

- return getDependency().value(NodeIDString.Q1_1, ScalarSubIDString.PROBABILITY) / 100;

- }

-

- @Override

- public Color getColor(){

- return Color.GREEN;

- }

-

-

- };

- }

-}

-package org.singinst.uf.model;

-

-public abstract class CompositeCalculation extends Calculation {

-

- private final Evaluable[] calculations;

-

- public CompositeCalculation(String description,

- Evaluable... calculations2) {

- super(description, false);

- this.calculations = calculations2;

- }

-

- public Evaluable[] getCalculations() {

- return calculations;

- }

-

-}

-package org.singinst.uf.model;

-

-public interface ConclusionReportGenerator {

- String getText(ScalarValueHolder scalarValueHolder, double value);

-

-}

-package org.singinst.uf.model;

-

-//import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-

-public class CumulativeHazardsRelation extends YearwiseCalculationRelation {

-

- public CumulativeHazardsRelation(Node node) {

- super(node);

- }

-

- @Override

- protected Calculation getCalculation(double year) {

- return new AtLeastOneHappensCalculation("Probability of nuclear or other science-disrupting catastrophe by year " + (int) year,

- probabilityOneCumulativeHazard(NodeIDString.Q1_7, "Independent probability of nuclear catastrophe by year ", year),

- probabilityOneCumulativeHazard(NodeIDString.Q1_8, "Independent probability of non-nuclear catastrophe by year ", year));

-

- }

-

- private Calculation probabilityOneCumulativeHazard(final String nodeId, final String description, final double year) {

- return new Calculation(description + (int) year) {

-

- @Override

- protected double rawEvaluate(StringBuilder htmlConsole) {

- double t0, t1;

- t0 = ModelUtil.EARLIEST_YEAR;

- t1 = ModelUtil.LATEST_YEAR;

- double klow, khigh;

- klow = NotablePercentile.PERCENTILE5.getValue();

- khigh = NotablePercentile.PERCENTILE95.getValue();

- double zlow, zhigh;

- zlow = NotablePercentile.PERCENTILE5.getOffset();

- zhigh = NotablePercentile.PERCENTILE95.getOffset();

-

- double y0low, y0high, y1low, y1high;

- y0low = getDependency().value(nodeId, ScalarSubIDString.yearPercentileID(t0, klow ))*Math.log(10);

- y0high = getDependency().value(nodeId, ScalarSubIDString.yearPercentileID(t0, khigh))*Math.log(10);

- y1low = getDependency().value(nodeId, ScalarSubIDString.yearPercentileID(t1, klow ))*Math.log(10);

- y1high = getDependency().value(nodeId, ScalarSubIDString.yearPercentileID(t1, khigh))*Math.log(10);

-

- double mean_init, stddev_init, mean_final, stddev_final;

- mean_init = MathUtil.linterp(zlow, zhigh, y0low, y0high, 0);

- stddev_init = (y0high-y0low)/(zhigh-zlow);

- mean_final = MathUtil.linterp(zlow, zhigh, y1low, y1high, 0);

- stddev_final = (y1high-y1low)/(zhigh-zlow);

-

- double[] clogp = MathUtil.clogMarginalOfExponentiallyVaryingHazardModel(

- t0, t1,

- mean_init, mean_final,

- stddev_init, stddev_final,

- new double[]{year}

- );

-

- return MathUtil.expc(clogp[0]);

- }

-

- };

- }

-

-}

-package org.singinst.uf.model;

-

-import java.awt.Color;

-

-import org.singinst.uf.math.MathUtil;

-

-public class CumulativeNormalDistributionCalculation extends Calculation {

-

- private final Evaluable mean;

- private final Evaluable stdDev;

- private final double x;

- private final Color color;

-

- /**

- *

- * @param description

- * @param mean

- * @param stdDev

- * @param x

- * @param display

- * The color to display the result of this calculation.

- */

- public CumulativeNormalDistributionCalculation(String description, Evaluable mean, Evaluable stdDev, double x, Color display) {

- super(description, false);

- this.mean = mean;

- this.stdDev = stdDev;

- this.x = x;

- this.color = display;

- }

-

- @Override

- public double rawEvaluate(StringBuilder htmlConsole) {

- StringBuilder nullBuilder = new StringBuilder();

- double meanValue = mean.evaluate(nullBuilder);

- double stdDevValue = stdDev.evaluate(nullBuilder);

- double retVal = MathUtil.cumulativeNormalDistribution(

- meanValue, stdDevValue, x);

- //htmlConsole.append("Cumulative probability (mean = " + meanValue + "; stddev = " + stdDevValue + ") of " + getDescription() + " at " + x);

- htmlConsole.append(getDescription().replace("\'", ""));

- return retVal;

- }

-

- @Override

- public Color getColor(){

- return color;

- }

-

-}

-package org.singinst.uf.model;

-

-//import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-

-public class DisruptionRelation extends YearwiseCalculationRelation {

-

- public DisruptionRelation(Node node) {

- super(node);

- }

-

- @Override

- protected Calculation getCalculation(double year) {

- return new AtLeastOneHappensCalculation("Probability that either AI, or a science-disrupting catastrophe, disrupts business as usual by year " + (int) year,

- ScalarValueHolder.findById(NodeIDString.C1_8, ScalarSubIDString.yearProbabilityID(year)),

- ScalarValueHolder.findById(NodeIDString.C1_9, ScalarSubIDString.yearProbabilityID(year)));

- }

-}

-package org.singinst.uf.model;

-

-public interface Evaluable {

- double evaluate(StringBuilder htmlConsole);

-}

-package org.singinst.uf.model;

-

-import org.singinst.uf.presenter.HtmlUtil;

-import org.singinst.uf.presenter.LineBounds;

-import org.singinst.uf.presenter.NumericEntry;

-

-public class GeneLearningExtreme extends YearExtremeNodeMetadataContentsFactory {

-

- protected GeneLearningExtreme(Node node, LineBounds rangeBounds,

- double year) {

- //super(node, rangeBounds, year, " log speedup after " + (int) year + " yrs", "", "early", "late");

- super(node, rangeBounds, year, " log research speedup after " + (int) year + " yrs", "", "small", "large");

- }

-

- @Override

- public ConclusionReportGenerator getConclusionGenerator() {

- return new ConclusionReportGenerator() {

- public String getText(ScalarValueHolder scalarValueHolder, double value) {

- return "The research speedup " + getYearString() + " years later has a 90% chance of being between " +

- HtmlUtil.green(display(getScalars().get(0).getScalarValueHolder().getValue())) +

- " and " + HtmlUtil.red(display(getScalars().get(2).getScalarValueHolder().getValue()));

- }

-

- private String display(double value) {

- return NumericEntry.formatAsScalar(Math.pow(10, value)) + "x";

- }

- };

-

- }

-

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Collection;

-import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.presenter.ScalarValuePointList;

-import org.singinst.uf.presenter.LineBounds;

-

-public class GeneLearningNodeMetadataContentsFactory {

-

- private final YearExtremeNodeMetadataContentsFactory extreme;

- private final List<ScalarSchema> scalarSchemata = new ArrayList<ScalarSchema>();

- private final ScalarRelation relation;

-

- private final List<PercentileDraggableLine> pointLists = new ArrayList<PercentileDraggableLine>();

-

- private final List<ScalarValueHolder> scalarValueList = new ArrayList<ScalarValueHolder>();

-

- public GeneLearningNodeMetadataContentsFactory(Node node) {

- final LineBounds range = new LineBounds(0, 4, true);

- final LineBounds domain = new LineBounds(0, ModelUtil.LATEST_IA_DELAY);

- extreme = new GeneLearningExtreme(node, range, ModelUtil.LATEST_IA_DELAY);

- scalarSchemata.addAll(extreme.getScalars());

- for (ScalarSchema scalarSchema : scalarSchemata) {

- scalarValueList.add(scalarSchema.getScalarValueHolder());

- }

-

- for (final NotablePercentile percentile : NotablePercentile.values()) {

- PercentileDraggableLine pointList = new PercentileDraggableLine(percentile) {

-

- @Override

- public void dragTo(SimplePoint point) {

- GeneLearningNodeMetadataContentsFactory.this.dragTo(percentile, point);

- }};

- pointLists.add(pointList);

- }

-

- relation = new ScalarRelation(new Axis(domain), new Axis(range)) {

-

- @Override

- public List<? extends ScalarValuePointList> getPointLists() {

- for (PercentileDraggableLine incidentLine : pointLists) {

- List<SimplePoint> points = new ArrayList<SimplePoint>();

- points.add(new SimplePoint(domain.getFirst(), range.getFirst()));

- points.add(new SimplePoint(extreme.getYear(),

- extreme.getScalarSchema(incidentLine.getPercentile()).getScalarValueHolder().getValue()));

- incidentLine.setHypothesisPoints(points);

- }

- return pointLists;

- }

-

- public List<? extends ConclusionReportGenerator> getConclusionGenerators() {

- List<ConclusionReportGenerator> retVal = new ArrayList<ConclusionReportGenerator>();

- retVal.add(extreme.getConclusionGenerator());

- return retVal;

- }

-

- public List<ScalarValueHolder> getScalarValues() {

- return scalarValueList ;

- }

- };

- }

-

- public Collection<? extends ScalarSchema> getScalarSchemata() {

- return scalarSchemata;

- }

-

- public ScalarRelation getRelation() {

- return relation;

- }

-

- public void dragTo(NotablePercentile percentile, SimplePoint point) {

- SimplePoint anchorPoint = new SimplePoint(0, 0);

- double newExtremeY = MathUtil.interpolate(

- anchorPoint, point, ModelUtil.LATEST_IA_DELAY);

- extreme.getScalarSchema(percentile).getScalarValueHolder().setValue(newExtremeY);

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Collection;

-import java.util.HashMap;

-import java.util.List;

-import java.util.Map;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.presenter.ScalarValuePointList;

-import org.singinst.uf.presenter.LineBounds;

-

-public class HazardRateNodeMetadataContentsFactory {

-

- private final List<ScalarSchema> scalarSchemata = new ArrayList<ScalarSchema>();

- private final ScalarRelation relation;

- private final Map<Double, YearExtremeNodeMetadataContentsFactory> extremeByYear = new HashMap<Double, YearExtremeNodeMetadataContentsFactory>();

-

- private final List<PercentileDraggableLine> pointLists = new ArrayList<PercentileDraggableLine>();

-

- private final List<ScalarValueHolder> conjectureList = new ArrayList<ScalarValueHolder>();

-

- private static final double MID_YEAR = MathUtil.average(ModelUtil.EARLIEST_YEAR, ModelUtil.LATEST_YEAR);

-

- public HazardRateNodeMetadataContentsFactory(Node node) {

- IncidentBounds incidentBounds = new IncidentBounds();

- LineBounds yearBounds = new LineBounds(ModelUtil.EARLIEST_YEAR, ModelUtil.LATEST_YEAR);

- final List<IncidentExtremeNodeMetadataContentsFactory> extremes = IncidentExtremeNodeMetadataContentsFactory.createList(node, incidentBounds, yearBounds);

- for (YearExtremeNodeMetadataContentsFactory extreme : extremes) {

- extremeByYear.put(extreme.getYear(), extreme);

- scalarSchemata.addAll(extreme.getScalars());

- }

- for (ScalarSchema scalarSchema : scalarSchemata) {

- conjectureList.add(scalarSchema.getScalarValueHolder());

- }

-

- for (NotablePercentile percentile : NotablePercentile.values()) {

- PercentileDraggableLine pointList = new IncidentLine(this, percentile);

- pointLists.add(pointList);

- }

-

- relation = new ScalarRelation(new Axis(yearBounds), new Axis(incidentBounds)) {

-

- @Override

- public List<? extends ScalarValuePointList> getPointLists() {

- for (PercentileDraggableLine incidentLine : pointLists) {

- List<SimplePoint> points = new ArrayList<SimplePoint>();

- for (YearExtremeNodeMetadataContentsFactory extreme : extremes) {

- points.add(new SimplePoint(extreme.getYear(),

- extreme.getScalarSchema(incidentLine.getPercentile()).getScalarValueHolder().getValue()));

- }

- incidentLine.setHypothesisPoints(points);

- }

- return pointLists;

- }

-

- public List<? extends ConclusionReportGenerator> getConclusionGenerators() {

- List<ConclusionReportGenerator> retVal = new ArrayList<ConclusionReportGenerator>();

- for (YearExtremeNodeMetadataContentsFactory extreme : extremes) {

- retVal.add(extreme.getConclusionGenerator());

- }

- return retVal;

- }

-

- public List<ScalarValueHolder> getScalarValues() {

- return conjectureList ;

- }

- };

- }

-

- public Collection<? extends ScalarSchema> getScalars() {

- return scalarSchemata;

- }

-

- public ScalarRelation getRelation() {

- return relation;

- }

-

- public void dragTo(NotablePercentile percentile, SimplePoint point) {

- boolean selectLeft = point.x < MID_YEAR;

- double year = selectLeft ? ModelUtil.EARLIEST_YEAR : ModelUtil.LATEST_YEAR;

- double anchorYear = selectLeft ? ModelUtil.LATEST_YEAR : ModelUtil.EARLIEST_YEAR;

- YearExtremeNodeMetadataContentsFactory extreme = extremeByYear.get(year);

- if (extreme == null) {

- throw new NullPointerException("" + year);

- }

- YearExtremeNodeMetadataContentsFactory anchorExtreme = extremeByYear.get(anchorYear);

- if (anchorExtreme == null) {

- throw new NullPointerException("" + year);

- }

- SimplePoint anchorPoint = new SimplePoint(

- anchorYear, anchorExtreme.getScalarSchema(percentile).getScalarValueHolder().getValue());

- double newExtremeY = MathUtil.interpolate(

- anchorPoint, point, year);

- extreme.getScalarSchema(percentile).getScalarValueHolder().setValue(newExtremeY);

- }

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.List;

-

-import org.singinst.uf.math.InvertableFunction;

-import org.singinst.uf.presenter.AxisSample;

-import org.singinst.uf.presenter.CanvasString;

-import org.singinst.uf.presenter.LineBounds;

-import org.singinst.uf.presenter.NumericEntry;

-

-public class IncidentBounds extends LineBounds {

-

- private static final InvertableFunction toDisplay = new IncidentFunction();

-

- private static final double LOWER_DISPLAY = 0.001;

- private static final double UPPER_DISPLAY = 99;

-

- public IncidentBounds() {

- super(fromDisplay(LOWER_DISPLAY), fromDisplay(UPPER_DISPLAY));

- }

-

- @Override

- public Iterable<AxisSample> getAxisSamples(boolean majorTick) {

- return samples(0.001, 0.01, 0.1, 1, 10, 50, 99);

- }

-

- private Iterable<AxisSample> samples(double... displays) {

- List<AxisSample> samples = new ArrayList<AxisSample>();

- for (double display : displays) {

- samples.add(new AxisSample(fromDisplay(display),

- new CanvasString("" + NumericEntry.formatAsScalar(display))));

- }

- return samples;

- }

-

- @Override

- public InvertableFunction toDisplay() {

- return toDisplay;

- }

-

- @Override

- public String userFormat(double value) {

- return "" + NumericEntry.formatAsScalar(toDisplay.apply(value));

- }

-

- private static double fromDisplay(double display) {

- return toDisplay.invert(display);

- }

-}

-package org.singinst.uf.model;

-

-import java.util.Arrays;

-import java.util.List;

-

-import org.singinst.uf.presenter.HtmlUtil;

-import org.singinst.uf.presenter.LineBounds;

-

-public class IncidentExtremeNodeMetadataContentsFactory extends YearExtremeNodeMetadataContentsFactory {

-

-

- protected IncidentExtremeNodeMetadataContentsFactory(Node node, LineBounds rangeBounds,

- double year) {

- super(node, rangeBounds, year, " chance in " + (int) year, "%", "small", "large");

- }

-

- public static List<IncidentExtremeNodeMetadataContentsFactory> createList(Node node, LineBounds rangeBounds, LineBounds yearBounds) {

- IncidentExtremeNodeMetadataContentsFactory incidentEarly = new IncidentExtremeNodeMetadataContentsFactory(node, rangeBounds, yearBounds.getFirst());

- IncidentExtremeNodeMetadataContentsFactory incidentLate = new IncidentExtremeNodeMetadataContentsFactory(node, rangeBounds, yearBounds.getSecond());

- return Arrays.asList(incidentEarly, incidentLate);

- }

-

- @Override

- public ConclusionReportGenerator getConclusionGenerator() {

- return new ConclusionReportGenerator() {

- public String getText(ScalarValueHolder scalarValueHolder, double value) {

- return "The likelihood at year " + getYearString() + " has a 90% chance of being between " +

- HtmlUtil.green(getScalars().get(0).getScalarValueHolder().getValue()) +

- " and " + HtmlUtil.red(getScalars().get(2).getScalarValueHolder().getValue());

- }

- };

-

- }

-

-

-}

-package org.singinst.uf.model;

-

-import org.singinst.uf.math.InvertableFunction;

-

-public class IncidentFunction extends InvertableFunction {

-

- @Override

- public double apply(double x) {

- double log1p = -1 * Math.pow(10, x);

- double probability = -1 * Math.expm1(log1p);

- return probability * 100;

- }

-

- @Override

- public double invert(double y) {

- double log1p = -1 * Math.log1p(-1 * y/100);

- return Math.log10(log1p);

- }

-}

-package org.singinst.uf.model;

-

-import org.singinst.uf.math.SimplePoint;

-

-public class IncidentLine extends PercentileDraggableLine {

-

- private final HazardRateNodeMetadataContentsFactory hazardRateNodeMetadataContentsFactory;

-

- public IncidentLine(HazardRateNodeMetadataContentsFactory hazardRateNodeMetadataContentsFactory, NotablePercentile percentile) {

- super(percentile);

- this.hazardRateNodeMetadataContentsFactory = hazardRateNodeMetadataContentsFactory;

- }

-

- @Override

- public void dragTo(SimplePoint point) {

- hazardRateNodeMetadataContentsFactory.dragTo(getPercentile(), point);

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.List;

-

-import org.singinst.uf.math.InvertableFunction;

-import org.singinst.uf.presenter.AxisSample;

-import org.singinst.uf.presenter.LineBounds;

-

-public class LogBounds extends LineBounds {

-

- private final double majorTickSpace;

- private final double minorTickSpace;

- private final double zero;

- private final double firstMajorTick;

- private final InvertableFunction toDisplay;

-

- public LogBounds(double firstMajorTick, double zero, double first, double second, double majorTickSpace, double minorTickSpace) {

- super(first, second);

- this.firstMajorTick = firstMajorTick;

- this.zero = zero;

- this.majorTickSpace = majorTickSpace;

- this.minorTickSpace = minorTickSpace;

- toDisplay = new PowerFunction(zero);

- }

-

- @Override

- public Iterable<AxisSample> getAxisSamples(boolean majorTick) {

- List<AxisSample> samples = new ArrayList<AxisSample>();

- double space;

- double display;

- if (majorTick) {

- space = majorTickSpace;

- display = firstMajorTick - zero;

- } else {

- space = minorTickSpace;

- display = Math.pow(10, getFirst());

- display += (minorTickSpace - display % minorTickSpace);

- }

- while (display <= Math.pow(10, getSecond())) {

- if (majorTick) {

- if (zero + display == 2050) {

- addSample(samples, 2010 - zero);

- addSample(samples, 2030 - zero);

- }

- if ((zero + display != 2350) && (zero + display != 2450)) {

- addSample(samples, display);

- }

- } else {

- samples.add(new AxisSample(Math.log10(display)));

- }

- display += space;

- }

- return samples;

- }

-

-

-

- @Override

- public InvertableFunction toDisplay() {

- return toDisplay;

- }

-

- @Override

- public String userFormat(double value) {

- return "" + Math.round(toDisplay.apply(value));

- }

-

- @Override

- public double constrain(double rounded) {

- double bounded = super.constrain(rounded);

- double display = toDisplay.apply(bounded);

- double intDisplay = Math.round(display);

- return toDisplay.invert(intDisplay);

- }

-

- private boolean addSample(List<AxisSample> samples, double display) {

- return samples.add(new AxisSample(Math.log10(display), getCanvasString(zero + display)));

- }

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.List;

-

-

-public class ModelUtil {

- protected static final int EARLIEST_YEAR = 2010;

- protected static final int LATEST_YEAR = 2070;

- public static final int EARLIEST_FAR_YEAR = 2010;

- public static final int LATEST_FAR_YEAR = 2500;

- public static final int EARLIEST_IA_YEAR = 2010;

- public static final int LATEST_IA_YEAR = 2050;

-

- public static final int ANCHOR_FAR_YEAR = 1950;

- public static final int LATEST_IA_DELAY = 50;

- public static final int RESEARCH_CAREER_DELAY = 20;

-

- public static final int LATEST_UNACCELERATED_YEAR = 5000;

-

- public static final double[] BASIC_MODEL_YEARS;

- static {

- BASIC_MODEL_YEARS = new double[LATEST_YEAR-EARLIEST_YEAR+1];

- for (int i=EARLIEST_YEAR; i<=LATEST_YEAR; ++i) {

- BASIC_MODEL_YEARS[i-EARLIEST_YEAR] = i;

- }

- }

- public static final double YEAR_STEPSIZE = 0.5;

- // TODO document this parameter in UI

- public static final double UNENHANCED_RESEARCHERS = 100000d;

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.Collection;

-import java.util.List;

-

-import org.singinst.uf.common.LogUtil;

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.presenter.DraggableLine;

-import org.singinst.uf.presenter.HtmlUtil;

-import org.singinst.uf.presenter.ScalarValuePointList;

-import org.singinst.uf.presenter.LineBounds;

-import org.singinst.uf.presenter.SimpleStyle;

-

-public class MooreAsymptote {

- private final ScalarSchema scalarSchema;

- private final LineBounds domain;

- private final ScalarValuePointList curvePointList;

- private final DraggableLine asymptotePointList;

-

- public MooreAsymptote(Node node, LineBounds domain, LineBounds asymptoteRange, NotablePercentile percentile) {

- asymptotePointList = new MooreDraggableLine(this, percentile);

- SimpleStyle style = new SimpleStyle(percentile.getColor());

- curvePointList = new ScalarValuePointList(style);

- this.domain = domain;

- int percentileValue = percentile.getValue();

- String name = "k" + percentileValue;

- scalarSchema = new ScalarSchema(node, name, asymptoteRange, "(log) FLOPS/$", HtmlUtil.style(style, percentile.getText("early", "late") + " stop: 10 to the "), "", null, true);

- }

-

- public Collection<? extends ScalarValuePointList> getPointLists() {

- return Arrays.asList(asymptotePointList(), curvePointList());

- }

-

- private ScalarValuePointList curvePointList() {

- List<SimplePoint> pointSamples = new ArrayList<SimplePoint>();

- for (double x : domain.getSpanningSamples(101)) {

- pointSamples.add(new SimplePoint(x, yFromX(x)));

- }

- curvePointList.setHypothesisPoints(pointSamples);

- return curvePointList;

- }

-

- private static final long T0 = 2008;

- private static final double P0_NON_LOG = 10280000;

- private static final double P0_LOG = Math.log(P0_NON_LOG);

- private static final double DOUBLING_TIME_IN_YEARS = 1.5;

- private static final double R = Math.log(2) / DOUBLING_TIME_IN_YEARS;

- private double yFromX(double x) {

- double exponentialGrowthFactor = Math.exp(R * (x - T0));

- double kNoLog = Math.pow(10, getConjecture().getValue());

- double numerator = (kNoLog) * P0_NON_LOG * exponentialGrowthFactor;

- double denominator = (kNoLog) + P0_NON_LOG * (exponentialGrowthFactor - 1);

- double functionalYNoLog = numerator / denominator;

- return Math.log10(functionalYNoLog);

- }

-

- private ScalarValuePointList asymptotePointList() {

- double y = getConjecture().getValue();

- asymptotePointList.setHypothesisPoints(Arrays.asList(

- new SimplePoint(domain.getLowerBound(), y),

- new SimplePoint(domain.getUpperBound(), y)));

- return asymptotePointList;

- }

-

- public ScalarValueHolder getConjecture() {

- return scalarSchema.getScalarValueHolder();

- }

-

- public ScalarSchema getScalar() {

- return scalarSchema;

- }

-

- public void setY(double y) {

- scalarSchema.getScalarValueHolder().setValue(y);

- }

-

- public static double unboundedLnFlopsPerDollarAtYear(double year) {

- double unboundedLnFlopsPerDollar = P0_LOG + (R * (year - T0));

- //LogUtil.info("at year " + year + ", natural log unbounded flops per dollar is " + unboundedLnFlopsPerDollar);

- return unboundedLnFlopsPerDollar;

- }

-}

-package org.singinst.uf.model;

-

-import java.util.List;

-

-public class MooreConstraint extends NormalConstraint {

-

- public MooreConstraint(List<ScalarSchema> scalarSchemata) {

- super(scalarSchemata, 8);

- }

-

-}

-package org.singinst.uf.model;

-

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.presenter.DraggableLine;

-import org.singinst.uf.presenter.SimpleStyle;

-

-public class MooreDraggableLine extends DraggableLine {

-

- private final MooreAsymptote mooreAsymptote;

-

- public MooreDraggableLine(MooreAsymptote mooreAsymptote, NotablePercentile percentile) {

- super(percentile.getPriority(), new SimpleStyle(percentile.getColor(), true));

- this.mooreAsymptote = mooreAsymptote;

- }

-

- @Override

- public void dragTo(SimplePoint point) {

- mooreAsymptote.setY(point.y);

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Collection;

-

-import org.singinst.uf.math.SimplePoint;

-

-public class MooresLawData {

-

- private static MooresLawData instance;

-

- public static MooresLawData getInstance() {

- if (instance == null) {

- instance = new MooresLawData();

- }

- return instance;

- }

-

- public MooresLawData() {

- points = new ArrayList<SimplePoint>();

- addPoint(1950, 0.00060, "SEAC");

- addPoint(1951, 0.00077, "UNIVAC_I");

- addPoint(1952, 0.00001, "Zuse-5");

- addPoint(1952, 0.00225, "IBM_CPC");

- addPoint(1953, 0.00062, "IBM_650");

- addPoint(1954, 0.00044, "EDVAC");

- addPoint(1955, 0.04476, "Whirlwind");

- addPoint(1955, 0.00346, "IBM_704");

- addPoint(1956, 0.00184, "Librascope_LGP-30");

- addPoint(1959, 0.01521, "IBM_7090");

- addPoint(1960, 0.00074, "IBM_1620");

- addPoint(1960, 0.11821, "DEC_PDP-1");

- addPoint(1961, 0.04032, "Atlas");

- addPoint(1962, 0.01444, "Burroughs_5000");

- addPoint(1963, 0.01665, "IBM_7040");

- addPoint(1963, 0.01586, "Honeywell_1800");

- addPoint(1964, 0.0845, "DEC_PDP-6");

- addPoint(1964, 0.2628, "CDC_6600");

- addPoint(1965, 0.456, "IBM_1130");

- addPoint(1966, 0.07925, "IBM_360/75");

- addPoint(1967, 0.06655, "IBM_360/65");

- addPoint(1968, 0.22008, "DEC_PDP-10");

- addPoint(1969, 0.45489, "CDC_7600");

- addPoint(1969, 2.73651, "DG_Nova");

- addPoint(1970, 0.06068, "GE-635");

- addPoint(1971, 0.20475, "SDS_920");

- addPoint(1972, 0.43683, "IBM_360/195");

- addPoint(1972, 1.2625, "Honeywell_700");

- addPoint(1973, 9.06353, "Prime_Computer_100");

- addPoint(1974, 1.05672, "IBM-370/168");

- addPoint(1974, 4.76, "MITS_Altair");

- addPoint(1975, 2.4346, "DG_Eclipse");

- addPoint(1975, 1.1914, "DEC-KL-10");

- addPoint(1976, 0.73067, "DEC_PDP-11/70");

- addPoint(1976, 4.11, "Cray-1");

- addPoint(1977, 4.49231, "Apple_II");

- addPoint(1977, 1.46, "DEC_VAX_11/780");

- addPoint(1977, 5.84, "TRS-80");

- addPoint(1977, 11.68, "Commodore_PET");

- addPoint(1978, 4.71, "CDC_IPL");

- addPoint(1979, 2.45, "Nanodata_VMX200");

- addPoint(1980, 13.23333, "TRS-80_M3");

- addPoint(1980, 6.40493, "Sun-1");

- addPoint(1981, 3.5624, "CDC_Cyber-205");

- addPoint(1981, 62.79570, "Vic_20");

- addPoint(1982, 31.62, "IBM_PC");

- addPoint(1982, 17.22825, "Sun-2");

- addPoint(1982, 186, "Commodore_64");

- addPoint(1983, 96, "TRS-80_M4");

- addPoint(1983, 7.6704, "Vax_11/750");

- addPoint(1984, 104.208, "Macintosh-128K");

- addPoint(1984, 5.6613, "Vax_11/785");

- addPoint(1985, 42.7656, "Cray-2");

- addPoint(1985, 67.63910, "L.Edge_XT-7.16");

- addPoint(1985, 100.74706, "Atari_800XL");

- addPoint(1986, 108.445, "Sun-3");

- addPoint(1986, 32.62872, "DEC_VAX_8650");

- addPoint(1986, 564.972, "MIT_XT-8");

- addPoint(1987, 456.66667, "Mac_II");

- addPoint(1987, 102.476, "Sun-4");

- addPoint(1988, 239.58042, "Mac-IIx");

- addPoint(1988, 763.15036, "CompuAdd_386-16");

- addPoint(1988, 1002.16327, "PC_Brand_386-25");

- addPoint(1989, 120.77255, "Wang_VS_10000");

- addPoint(1989, 359.07621, "Macintosh_SE30");

- addPoint(1989, 304.98, "Solbourne_5/500");

- addPoint(1990, 193.24719, "Stardent_3000");

- addPoint(1990, 2715.51724, "Dell_320LX");

- addPoint(1990, 638.36255, "Mac_IIfx");

- addPoint(1990, 2386.36364, "Amiga_3000");

- addPoint(1991, 5205.46154, "Gateway-486DX2/66");

- addPoint(1991, 4212.52941, "ACT_468/33");

- addPoint(1991, 4381.32768, "Mac-Quadra-900");

- addPoint(1992, 6238.07143, "AST_Bravo");

- addPoint(1992, 3588.1, "IBM_PS/2_55-041");

- addPoint(1992, 3074.70833, "NEC_Powermate");

- addPoint(1993, 5053.25, "IBM_Valuepoint");

- addPoint(1993, 8861.85714, "Acer_Power");

- addPoint(1993, 3989.72414, "DECpc_LPv");

- addPoint(1994, 10367.5, "IBM_433/DX/Si");

- addPoint(1994, 11583, "Gateway_2000_486");

- addPoint(1994, 24663.67713, "PowerMac_7100/66");

- addPoint(1995, 33083.27082, "PowerMac_8500/120");

- addPoint(1995, 27741.08322, "PowerMac_9500/132");

- addPoint(1995, 25725, "Intel_Xpress/60");

- addPoint(1996, 34822, "Gateway_P5-75");

- addPoint(1996, 68922.61002, "Power_Tower_180e");

- addPoint(1996, 40386.79560, "PowerMac_7600/132");

- addPoint(1997, 91861.64802, "Gateway_G6-200");

- addPoint(1997, 116100, "Power_Center_210");

- addPoint(1997, 193500, "Mac_G3/266");

- addPoint(1998, 272076.92308, "iMac_G3/233");

- addPoint(1998, 165933.33333, "AcerPower8000/450B");

- addPoint(1998, 222130.43478, "Mac_G3/333");

- addPoint(1999, 301500, "Pentium_II/455");

- addPoint(1999, 263712, "Pentium_III/500");

- addPoint(1999, 275289.6, "Mac_G4/450");

- addPoint(2000, 75545.45455, "IBM_ASCI_White");

- addPoint(2000, 16620000.0, "Sony_Playstation_II");

- addPoint(2000, 356142.85714, "Mac_G4/500_dual");

- addPoint(2001, 392840, "CerfCube");

- addPoint(2001, 444080, "Mac_G4/867");

- addPoint(2001, 945093.33333, "Dell_Workst_340/2G");

- addPoint(2002, 596750, "iMac_G4/700");

- addPoint(2002, 1562400, "Dell_Workst_340/2.5");

- addPoint(2002, 1198997.33333, "Athlon_XP_2.6GHz");

- addPoint(2003, 1108750, "Mac_G4/Dual-1.25GHz");

- addPoint(2003, 1574957.2, "Dell_D_8300_P4/3.0");

- addPoint(2003, 1878666, "Mac_G5/Dual-2.0");

- addPoint(2004, 3871750, "Dell_P4-530_3.0");

- addPoint(2004, 2602857.14286, "Mac_G5/Dual-2.5");

- addPoint(2004, 3036666.66667, "VAtech_SysX_2.3G");

- addPoint(2004, 3097400, "Athlon64_FX55-2.6");

- addPoint(2005, 3485400, "Mac_Mini_G4-1.25");

- addPoint(2005, 4396000, "Opteron_252-2.6");

- addPoint(2005, 5382857.14286, "Mac_G5/Quad-2.5");

- addPoint(2006, 8576470.58824, "iMac-Core_Duo_2.0x2");

- addPoint(2006, 11664000, "Mac_Pro_Xeon_2.66x4");

- addPoint(2006, 10935000, "Mac_Mini_Duo_1.83x2");

- addPoint(2007, 12500000, "Mac_Pro/2_3GHzx8");

- addPoint(2007, 10000000, "Sun_Blackbox");

- addPoint(2007, 10769230.76923, "IBM_Blue_Gene/P");

- }

-

- private void addPoint(int year, double flopsPerDollar, String name) {

- points.add(new SimplePoint(year, Math.log10(flopsPerDollar)));

- }

-

- public final Collection<SimplePoint> points;

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Collections;

-import java.util.List;

-

-import org.singinst.uf.presenter.HtmlUtil;

-import org.singinst.uf.presenter.ScalarValuePointList;

-import org.singinst.uf.presenter.LineBounds;

-

-class MooresLawNodeMetadataContentsFactory {

- private final List<MooreAsymptote> asymptotes = new ArrayList<MooreAsymptote>();

- private final List<ScalarSchema> scalarSchemata = new ArrayList<ScalarSchema>();

- private final List<ScalarValueHolder> scalarValueHolders = new ArrayList<ScalarValueHolder>();

- private final ScalarRelation relation;

-

- public MooresLawNodeMetadataContentsFactory(Node node) {

- LineBounds yearDomain = new LineBounds(1950, 2070);

- Axis year = new Axis(yearDomain);

- Axis flopsPerDollar = new Axis(new LineBounds(-4, 18, true));

-

- int asymptoteLowerBound = 8;

- for (NotablePercentile percentile : NotablePercentile.values()) {

- LineBounds asymptoteRange = new LineBounds(asymptoteLowerBound, 18);

- MooreAsymptote asymptote = new MooreAsymptote(node, yearDomain, asymptoteRange, percentile);

- asymptotes.add(asymptote);

- scalarSchemata.add(asymptote.getScalar());

- scalarValueHolders.add(asymptote.getConjecture());

- asymptoteLowerBound += 2;

- }

- new MooreConstraint(getScalars()).constrain();

-

- relation = new ScalarRelation(year, flopsPerDollar, MooresLawData.getInstance().points) {

-

- public List<? extends ConclusionReportGenerator> getConclusionGenerators() {

- return Collections.singletonList(new ConclusionReportGenerator() {

-

- public String getText(ScalarValueHolder scalarValueHolder, double value) {

- return "The limiting value of log(FLOPS/dollar) has a 90% chance of being between " +

- HtmlUtil.green(getScalars().get(0).getScalarValueHolder().getValue()) +

- " and " + HtmlUtil.red(getScalars().get(2).getScalarValueHolder().getValue());

- }

-

- });

- }

-

- public List<ScalarValueHolder> getScalarValues() {

- return scalarValueHolders;

- }

-

- @Override

- public List<ScalarValuePointList> getPointLists() {

- List<ScalarValuePointList> retVal = new ArrayList<ScalarValuePointList>();

- for (MooreAsymptote asymptote : asymptotes) {

- retVal.addAll(asymptote.getPointLists());

- }

- return retVal;

- }

- };

-

- }

- public ScalarRelation getRelation() {

- return relation;

- }

- public List<ScalarSchema> getScalars() {

- return scalarSchemata;

- }

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.List;

-import org.singinst.uf.presenter.HtmlUtil;

-

-import org.singinst.uf.common.StringUtil;

-

-public class MultiplicationCalculation extends Calculation {

-

- private final Evaluable[] calculationArray;

-

- public MultiplicationCalculation(String description,

- Evaluable... calculationArray) {

- super(description, false);

- this.calculationArray = calculationArray;

- }

-

- @Override

- public double rawEvaluate(StringBuilder htmlConsole) {

- double retVal = 1;

- List<String> readableSubCalculationResultList = new ArrayList<String>();

- for (Evaluable subCalculation : calculationArray) {

- double subCalculationResult = subCalculation.evaluate(htmlConsole);

- retVal *= subCalculationResult;

- String str = HtmlUtil.htmlcolorFromColor(((Calculation)subCalculation).getColor(), "" + subCalculationResult);

- readableSubCalculationResultList.add(str);

- }

- htmlConsole.append(getDescription() + " = " + StringUtil.join(" * ", readableSubCalculationResultList));

- return retVal;

- }

-

-

-}

-package org.singinst.uf.model;

-

-import java.util.HashMap;

-import java.util.Map;

-

-public class NeedsCalcCache {

-

- public Map<ScalarValueHolder, Boolean> cache = new HashMap<ScalarValueHolder, Boolean>();

-

- public boolean needsCalc(ScalarValueHolder scalarValueHolder) {

- Boolean needsCalc = cache.get(scalarValueHolder);

- if (needsCalc == null) {

- needsCalc = scalarValueHolder.needsCalc(this);

- cache.put(scalarValueHolder, needsCalc);

- }

- return needsCalc;

- }

-

-}

-package org.singinst.uf.model;

-

-import java.awt.Color;

-import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-

-public class NeuromorphicAiRelation extends YearwiseCalculationRelation {

-

- public NeuromorphicAiRelation(Node node) {

- super(node);

- }

-

- @Override

- protected Calculation getCalculation(final double year) {

- Calculation agiPossible =

- probabilityAiIsPossible();

- Calculation sufficientHardware =

- new Calculation("Probability of sufficient hardware in " + (int) year) {

-

- protected double rawEvaluate(StringBuilder htmlConsole) {

- return probabilityOfSufficientHardware(year);

- }

-

- @Override

- public Color getColor(){

- return Color.BLUE;

- }

- };

- Calculation sufficientBrainImaging = probabilityOfSufficientBrainImaging(year);

- return new MultiplicationCalculation(

- "Probability of a neuromorphic AI on or before " + (int) year,

- agiPossible,

- sufficientHardware,

- sufficientBrainImaging

- );

- }

-

- private Calculation probabilityOfSufficientBrainImaging(final double year) {

- return new CumulativeNormalDistributionCalculation(

- "'Probability of sufficient brain imaging in " + year + "'",

- ScalarValueHolder.findById(NodeIDString.Q1_5, ScalarSubIDString.MEAN),

- ScalarValueHolder.findById(NodeIDString.Q1_5, ScalarSubIDString.STD_DEV),

- Math.log10(year - ModelUtil.ANCHOR_FAR_YEAR),

- Color.RED);

- }

-

- private double probabilityOfSufficientHardware(double year) {

- double unboundedLogFlopsPerDollarForYear = MooreAsymptote.unboundedLnFlopsPerDollarAtYear(year);

- double meanLogComputingEfficiencyNeeded = meanComputingEfficiencyNeeded() * Math.log(10);

- double stdDevLogComputingEfficiencyNeeded = stdDevComputingEfficiencyNeeded() * Math.log(10);

- double muX = muX() * Math.log(10);

- double sigmaX = sigmaX() * Math.log(10);

-

- double numerator = 0;

- double denominator = 0;

- double z;

- org.singinst.uf.common.LogUtil.info(String.format("probabilityOfSufficientHardware called at year %5g, time %20.15g", (double)year, ((double) System.currentTimeMillis()) / 1000));

- if (meanLogComputingEfficiencyNeeded + 10.0 * stdDevLogComputingEfficiencyNeeded > unboundedLogFlopsPerDollarForYear) {

- /* brute force midpoint integral, hope for Gaussian Euler-Maclaurin edge term suppression */

- double logComputingEfficiency;

- for (int s = -100; s <= 100; s += 1) {

- z = (double)s/10;

- double slice = Math.exp(-0.5 * z * z);

- logComputingEfficiency = h(muX + sigmaX * z, unboundedLogFlopsPerDollarForYear);

- numerator += slice * MathUtil.cumulativeNormalDistribution(meanLogComputingEfficiencyNeeded, stdDevLogComputingEfficiencyNeeded, logComputingEfficiency);

- denominator += slice;

-// org.singinst.uf.common.LogUtil.info(String.format("at year %5g, z = %+8g (x = %+12.7g) d = %10g, num = %g, denom = %g", (double)year, z, sigmaX() * z, d(sigmaX() * z + muX(), year), numerator, denominator));

- }

- org.singinst.uf.common.LogUtil.info(String.format("needed = %8g +/- %8g, bound = %8g +/- %8g, unlimited = %8g", meanLogComputingEfficiencyNeeded, stdDevLogComputingEfficiencyNeeded, muX, sigmaX, unboundedLogFlopsPerDollarForYear));

- } else {

- double logComputingEfficiencyUpperBoundMinimum;

- for (int s = -100; s <= 100; s += 1) {

- z = (double)s/10;

- double slice = Math.exp(-0.5 * z * z);

- logComputingEfficiencyUpperBoundMinimum = hinv(meanLogComputingEfficiencyNeeded + stdDevLogComputingEfficiencyNeeded * z, unboundedLogFlopsPerDollarForYear);

- numerator += slice * MathUtil.cumulativeNormalDistribution(-muX, sigmaX, -logComputingEfficiencyUpperBoundMinimum);

- denominator += slice;

- }

- org.singinst.uf.common.LogUtil.info(String.format("bound = %8g +/- %8g, minimum = %8g +/- %8g, unlimited = %8g", muX, sigmaX, meanLogComputingEfficiencyNeeded, stdDevLogComputingEfficiencyNeeded, unboundedLogFlopsPerDollarForYear));

- }

- org.singinst.uf.common.LogUtil.info(String.format("probabilityOfSufficientHardware called at year %5g, time %20.15g, result %20.16g", (double)year, ((double) System.currentTimeMillis()) / 1000, numerator / denominator));

- return numerator / denominator;

- }

-

- private double meanComputingEfficiencyNeeded() {

- // flops divided by dollars available

- return getDependency().value(NodeIDString.Q1_3, ScalarSubIDString.MEAN) -

- getDependency().value(NodeIDString.Q1_4, ScalarSubIDString.MEAN);

- }

-

- private double stdDevComputingEfficiencyNeeded() {

- double stdDev1_3 = getDependency().value(NodeIDString.Q1_3, ScalarSubIDString.STD_DEV);

- double stdDev1_4 = getDependency().value(NodeIDString.Q1_4, ScalarSubIDString.STD_DEV);

- return Math.sqrt(

- stdDev1_3 * stdDev1_3 + stdDev1_4 * stdDev1_4);

- }

-

- /**

- * sum conjugated with logarithm, conjugated with negation

- * @param hypothetical log hardware production efficiency upper bound

- * @param hypothetical log hardware production efficiency if no upper bound

- * @return hypothetical log hardware production efficiency after effects of upper bound

- */

- static double h(double x, double unboundedLogFlopsPerDollarForYear) {

- //return Math.log(exp(k) * exp(unboundedLogFlopsPerDollarForYear) / (exp(k) + exp(unboundedLogFlopsPerDollarForYear)));

- if (x > unboundedLogFlopsPerDollarForYear) {

- return unboundedLogFlopsPerDollarForYear - Math.log1p(Math.exp(unboundedLogFlopsPerDollarForYear - x));

- } else {

- return x - Math.log1p(Math.exp(x - unboundedLogFlopsPerDollarForYear));

- }

- }

-

- /**

- *

- * @param hypothetical log hardware production efficiency after effects of upper bound

- * @param hypothetical log hardware production efficiency if no upper bound

- * @return hypothetical log hardware production efficiency upper bound

- */

- static double hinv(double h, double unboundedLogFlopsPerDollarForYear) {

- if (h >= unboundedLogFlopsPerDollarForYear) {

- return Double.POSITIVE_INFINITY;

- } else if ((h + Math.log(2)) >= unboundedLogFlopsPerDollarForYear) {

- return unboundedLogFlopsPerDollarForYear - Math.log(Math.expm1(unboundedLogFlopsPerDollarForYear-h));

- } else {

- return h - Math.log1p(-Math.exp(h-unboundedLogFlopsPerDollarForYear));

- }

- }

-

- private double muX() {

- return getDependency().value(

- NodeIDString.Q1_2, ScalarSubIDString.K50);

- }

-

- private double sigmaX() {

- return (muX() - getDependency().value(

- NodeIDString.Q1_2, ScalarSubIDString.K5)) / MathUtil.NINETY_FIVE_PERCENTILE;

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.List;

-

-public class Node {

-

- private final String idString;

- private final List<NodeMetadata> dependencies;

-

- public Node(String name, List<NodeMetadata> dependencies) {

- this.idString = name;

- this.dependencies = dependencies;

-

- }

-

- public String getIdString() {

- return idString;

- }

-

- public List<NodeMetadata> getDependencies() {

- return dependencies;

- }

-

-}

-package org.singinst.uf.model;

-

-public class NodeIDString {

- public static final String Q1_1 = "Q1";

- public static final String Q1_2 = "Q2";

- public static final String Q1_3 = "Q3";

- public static final String Q1_4 = "Q4";

- public static final String Q1_5 = "Q5";

- public static final String C1_5 = "A5";

- public static final String Q1_6 = "Q6";

- public static final String C1_6 = "A6";

- public static final String Q1_7 = "Q7";

- public static final String Q1_8 = "Q8";

- public static final String C1_8 = "A8";

- public static final String Q1_9_1 = "Q9-1";

- public static final String Q1_9_2 = "Q9-2";

- public static final String Q1_9_3 = "Q9-3";

- public static final String Q1_9_4 = "Q9-4";

- public static final String Q1_9_5 = "Q9-5";

- public static final String C1_9 = "A9";

- public static final String C1_10 = "A10";

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Collection;

-import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-

-public class NodeMetadata {

- private final Node node;

- private final UiText uiText;

- private final List<ScalarSchema> scalarSchemata;

- private final List<ScalarSchema> simpleScalars; // never used

- private final List<ScalarRelation> scalarRelations;

- private final List<ScalarValueHolder> scalarValueHolders;

-

- public NodeMetadata(Node node, UiText uiText, List<ScalarSchema> scalarSchemata, List<ScalarSchema> simpleScalars, List<ScalarRelation> scalarRelations) {

- this.node = node;

- this.uiText = uiText;

- this.scalarSchemata = scalarSchemata;

- this.simpleScalars = simpleScalars;

- this.scalarRelations = scalarRelations;

- this.scalarValueHolders = new ArrayList<ScalarValueHolder>();

- for (ScalarSchema schema : scalarSchemata) {

- scalarValueHolders.add(schema.getScalarValueHolder());

- }

- }

-

- public List<ScalarSchema> getScalars() {

- return scalarSchemata;

- }

-

- public Node getNode() {

- return node;

- }

-

- public List<ScalarRelation> getScalarRelations() {

- return scalarRelations;

- }

-

- public List<ScalarSchema> getSimpleScalars() {

- return simpleScalars;

- }

-

- public UiText getUserText() {

- return uiText;

- }

-

- public Object monte() {

- if (!simpleScalars.isEmpty()) {

- return simpleScalars.get(0).monte();

- } else if (scalarSchemata.size() == 2) {

- double mean = scalarSchemata.get(0).getScalarValueHolder().getValue();

- double stdDev = scalarSchemata.get(1).getScalarValueHolder().getValue();

- return MathUtil.inverseCumulativeProbability(mean, stdDev, MathUtil.RANDOM.nextFloat());

- } else {

- double k5 = scalarSchemata.get(0).getScalarValueHolder().getValue();

- double k50 = scalarSchemata.get(1).getScalarValueHolder().getValue();

- return MathUtil.inverseCumulativeProbability(

- k50, (k50 - k5) / MathUtil.NINETY_FIVE_PERCENTILE,

- MathUtil.RANDOM.nextFloat());

- }

- }

-

- public List<ScalarValueHolder> getScalarValueHolders() {

- return scalarValueHolders;

- }

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.Collections;

-import java.util.List;

-

-import org.singinst.uf.presenter.LineBounds;

-

-class NodeMetadataBuilder {

- private final Node node;

- private UiText uiText;

- private final List<ScalarSchema> scalarSchemata = new ArrayList<ScalarSchema>();

- private final List<ScalarSchema> simpleScalars = new ArrayList<ScalarSchema>();

- private final List<ScalarRelation> scalarRelations = new ArrayList<ScalarRelation>();

-

- public NodeMetadataBuilder(String name) {

- this(name, Collections.<NodeMetadata>emptyList());

- }

-

- public NodeMetadataBuilder(String name, List<NodeMetadata> dependencies) {

- this(new Node(name, dependencies));

- }

-

- public NodeMetadataBuilder(Node node) {

- this.node = node;

- }

-

- public NodeMetadataBuilder setBuilderUserText(UiText uiText) {

- this.uiText = uiText;

- return this;

- }

-

- public NodeMetadataBuilder buildNodeTypeAsProbability(ConclusionReportGenerator... generators) {

- LineBounds bounds = new LineBounds(0, 100);

- ScalarSchema scalarSchema = new ScalarSchema(node, ScalarSubIDString.PROBABILITY, bounds, "%", "Probability: ", "", bounds, true);

- scalarSchema.getConclusionGenerators().addAll(Arrays.asList(generators));

- scalarSchemata.add(scalarSchema);

- simpleScalars.add(scalarSchema);

- return this;

- }

-

- public NodeMetadata build() {

- return new NodeMetadata(node, uiText, scalarSchemata, simpleScalars, scalarRelations);

- }

-

- public NodeMetadataBuilder buildNodeTypeAsMooresLaw() {

- MooresLawNodeMetadataContentsFactory mooresLawNodeMetadataContentsFactory = new MooresLawNodeMetadataContentsFactory(node);

- scalarSchemata.addAll(mooresLawNodeMetadataContentsFactory.getScalars());

- scalarRelations.add(mooresLawNodeMetadataContentsFactory.getRelation());

- return this;

- }

-

- public NodeMetadataBuilder buildNodeTypeAsHazardRate() {

- HazardRateNodeMetadataContentsFactory hazardRateNodeMetadataContentsFactory = new HazardRateNodeMetadataContentsFactory(node);

- scalarSchemata.addAll(hazardRateNodeMetadataContentsFactory.getScalars());

- scalarRelations.add(hazardRateNodeMetadataContentsFactory.getRelation());

- return this;

- }

-

- public NodeMetadataBuilder buildNodeTypeAsGeneLearning() {

- GeneLearningNodeMetadataContentsFactory geneLearningNodeMetadataContentsFactory = new GeneLearningNodeMetadataContentsFactory(node);

- scalarSchemata.addAll(geneLearningNodeMetadataContentsFactory.getScalarSchemata());

- scalarRelations.add(geneLearningNodeMetadataContentsFactory.getRelation());

- return this;

- }

-

- public NodeMetadataBuilder buildNodeTypeAsOtherIa() {

- OtherIaNodeMetadataContentsFactory otherIaNodeMetadataPartialPrototype = new OtherIaNodeMetadataContentsFactory(node);

- scalarSchemata.addAll(otherIaNodeMetadataPartialPrototype.getScalars());

- scalarRelations.add(otherIaNodeMetadataPartialPrototype.getRelation());

- return this;

- }

-

- public NodeMetadataBuilder buildNodeTypeAsResearcher() {

- ResearchersNodeMetadataContentsFactory researchersNodeMetadataContentsFactory = new ResearchersNodeMetadataContentsFactory(node);

- scalarSchemata.addAll(researchersNodeMetadataContentsFactory.getScalars());

- scalarRelations.add(researchersNodeMetadataContentsFactory.getRelation());

- return this;

- }

-

- public NodeMetadataBuilder buildNodeTypeAsLogNormal(String units, double lowerBound, double upperBound) {

- NormalNodeMetadataContentsFactory logNormal = new NormalNodeMetadataContentsFactory(node, units, new LineBounds(lowerBound, upperBound, true), "");

- scalarSchemata.addAll(logNormal.getScalars());

- scalarRelations.add(logNormal.getRelation());

- return this;

- }

-

- public NodeMetadataBuilder buildNodeTypeAsYearLogNormal(int firstMajorTick, int boundaryYear, int lowerYear, int upperYear) {

- double lowerBound = Math.log10(lowerYear - boundaryYear);

- double upperBound = Math.log10(upperYear - boundaryYear);

- int majorTickSpace = upperYear == ModelUtil.LATEST_IA_YEAR ? 5 : 50;

- LineBounds bounds = new LogBounds(firstMajorTick, boundaryYear, lowerBound, upperBound, majorTickSpace, 10);

- //NormalNodeMetadataContentsFactory yearNormal = new NormalNodeMetadataContentsFactory(node, "(log) years after " + boundaryYear, bounds) {

- NormalNodeMetadataContentsFactory yearNormal = new NormalNodeMetadataContentsFactory(node, "years after " + boundaryYear, bounds, "10 to the") {

-

- @Override

- protected String meanUnits() {

- return "year";

- }

-

- };

- scalarSchemata.addAll(yearNormal.getScalars());

- scalarRelations.add(yearNormal.getRelation());

- return this;

- }

-

- public NodeMetadataBuilder addScalarRelation(ScalarRelation relation) {

- scalarRelations.add(relation);

- return this;

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.Arrays;

-import java.util.List;

-

-import org.singinst.uf.presenter.NumericEntry;

-

-public class NodeMetadataFactory {

- public static List<NodeMetadata> createTheNetwork() {

- NodeMetadata q1_1 = constructQ1_1Builder().build();

- NodeMetadata q1_2 = constructQ1_2Builder().build();

- NodeMetadata q1_3 = constructQ1_3Builder().build();

- NodeMetadata q1_4 = constructQ1_4Builder().build();

- NodeMetadata q1_5 = constructQ1_5Builder().build();

- NodeMetadata c1_5 = constructC1_5Builder(Arrays.asList(q1_1, q1_2, q1_3, q1_4, q1_5)).build();

- NodeMetadata q1_6 = constructQ1_6Builder().build();

- NodeMetadata c1_6 = constructC1_6Builder(Arrays.asList(c1_5, q1_6)).build();

- NodeMetadata q1_7 = constructQ1_7Builder().build();

- NodeMetadata q1_8 = constructQ1_8Builder().build();

- NodeMetadata c1_8 = constructC1_8Builder(Arrays.asList(q1_7, q1_8)).build();

- NodeMetadata q1_9_1 = constructQ1_9_1Builder().build();

- NodeMetadata q1_9_2 = constructQ1_9_2Builder().build();

- NodeMetadata q1_9_3 = constructQ1_9_3Builder().build();

- NodeMetadata q1_9_4 = constructQ1_9_4Builder().build();

- NodeMetadata q1_9_5 = constructQ1_9_5Builder().build();

- //NodeMetadata c1_9 = constructQ1_9Builder(Arrays.asList(q1_9_1, q1_9_2, q1_9_3, q1_9_4, q1_9_5, c1_6, c1_5)).build();

- NodeMetadata c1_9 = constructQ1_9Builder(Arrays.asList(q1_1, q1_2, q1_3, q1_4, q1_5, q1_9_1, q1_9_2, q1_9_3, q1_9_4, q1_9_5, c1_5, c1_6)).build();

- NodeMetadata c1_10 = constructC1_10Builder(Arrays.asList(c1_8, c1_9)).build();

-

- setNodeScalars(q1_1, 99);

- setNodeScalars(q1_2, 10, 11, 12);

- setNodeScalars(q1_3, 12.5, 2.25);

- setNodeScalars(q1_4, 5, 1);

- //setNodeScalars(q1_5, 2018, 0.005);

- //setNodeScalars(q1_6);

-

- return Arrays.asList(new NodeMetadata[] {

- q1_1,

- q1_2,

- q1_3,

- q1_4,

- q1_5,

- c1_5,

- q1_6,

- c1_6,

- q1_7,

- q1_8,

- c1_8,

- q1_9_1,

- q1_9_2,

- q1_9_3,

- q1_9_4,

- q1_9_5,

- c1_9,

- c1_10

- });

- }

-

- private static void setNodeScalars(NodeMetadata qNotOnly1_1GoesHere, double... dArray) {

- int i = 0;

- for (double d : dArray) {

- ScalarValueHolder scalarValueHolder = qNotOnly1_1GoesHere.getScalars().get(i).getScalarValueHolder();

- scalarValueHolder.setValue(d);

- i++;

- }

-

- }

-

- private static NodeMetadataBuilder constructQ1_1Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_1)

- .setBuilderUserText(UiTextConstant.Q1_1_TEXT)

- .buildNodeTypeAsProbability(new ConclusionReportGenerator() {

-

- public String getText(ScalarValueHolder scalarValueHolder, double value) {

- return "The probability AI is possible in principle is " + NumericEntry.formatAsScalar(value) + "%";

- }

-

- },

- new ConclusionReportGenerator() {

-

- public String getText(ScalarValueHolder scalarValueHolder, double value) {

- return "The probability AI is not possible in principle is " + NumericEntry.formatAsScalar(100 - value) + "%";

- }

-

- });

- }

-

- private static NodeMetadataBuilder constructQ1_2Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_2)

- .setBuilderUserText(UiTextConstant.Q1_2_TEXT)

- .buildNodeTypeAsMooresLaw();

- }

-

- private static NodeMetadataBuilder constructQ1_3Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_3)

- .setBuilderUserText(UiTextConstant.Q1_3_TEXT)

- .buildNodeTypeAsLogNormal("flops", 7, 30);

- }

-

- private static NodeMetadataBuilder constructQ1_4Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_4)

- .setBuilderUserText(UiTextConstant.Q1_4_TEXT)

- .buildNodeTypeAsLogNormal("USD", 2, 11);

- }

-

- private static NodeMetadataBuilder constructQ1_5Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_5)

- .setBuilderUserText(UiTextConstant.Q1_5_TEXT)

- .buildNodeTypeAsYearLogNormal(2050, ModelUtil.ANCHOR_FAR_YEAR, ModelUtil.EARLIEST_FAR_YEAR, ModelUtil.LATEST_FAR_YEAR);

- }

-

- private static NodeMetadataBuilder constructC1_5Builder(List<NodeMetadata> dependencies) {

- Node node = new Node(NodeIDString.C1_5, dependencies);

- return new NodeMetadataBuilder(node)

- .setBuilderUserText(UiTextConstant.C1_5_TEXT)

- .addScalarRelation(new NeuromorphicAiRelation(node));

- }

-

- private static NodeMetadataBuilder constructQ1_6Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_6)

- .setBuilderUserText(UiTextConstant.Q1_6_TEXT)

- .buildNodeTypeAsYearLogNormal(2050, ModelUtil.ANCHOR_FAR_YEAR, ModelUtil.EARLIEST_FAR_YEAR, ModelUtil.LATEST_FAR_YEAR);

- }

-

- private static NodeMetadataBuilder constructC1_6Builder(List<NodeMetadata> dependencies) {

- Node node = new Node(NodeIDString.C1_6, dependencies);

- return new NodeMetadataBuilder(node)

- .setBuilderUserText(UiTextConstant.C1_6_TEXT)

- .addScalarRelation(new AiRelation(node));

- }

-

- private static NodeMetadataBuilder constructQ1_7Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_7)

- .setBuilderUserText(UiTextConstant.Q1_7_TEXT)

- .buildNodeTypeAsHazardRate();

- }

-

- private static NodeMetadataBuilder constructQ1_8Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_8)

- .setBuilderUserText(UiTextConstant.Q1_8_TEXT)

- .buildNodeTypeAsHazardRate();

- }

-

- private static NodeMetadataBuilder constructC1_8Builder(List<NodeMetadata> dependencies) {

- Node node = new Node(NodeIDString.C1_8, dependencies);

- return new NodeMetadataBuilder(node)

- .setBuilderUserText(UiTextConstant.C1_8_TEXT)

- .addScalarRelation(new CumulativeHazardsRelation(node));

- }

-

- private static NodeMetadataBuilder constructQ1_9_1Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_9_1)

- .setBuilderUserText(UiTextConstant.Q1_9_1_TEXT)

- .buildNodeTypeAsYearLogNormal(2010, ModelUtil.ANCHOR_FAR_YEAR, ModelUtil.EARLIEST_IA_YEAR, ModelUtil.LATEST_IA_YEAR);

- }

-

- private static NodeMetadataBuilder constructQ1_9_2Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_9_2)

- .setBuilderUserText(UiTextConstant.Q1_9_2_TEXT)

- .buildNodeTypeAsYearLogNormal(2010, ModelUtil.ANCHOR_FAR_YEAR, ModelUtil.EARLIEST_IA_YEAR, ModelUtil.LATEST_IA_YEAR);

- }

-

- private static NodeMetadataBuilder constructQ1_9_3Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_9_3)

- .setBuilderUserText(UiTextConstant.Q1_9_3_TEXT)

- .buildNodeTypeAsGeneLearning();

- }

-

- private static NodeMetadataBuilder constructQ1_9_4Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_9_4)

- .setBuilderUserText(UiTextConstant.Q1_9_4_TEXT)

- .buildNodeTypeAsResearcher();

- }

-

- private static NodeMetadataBuilder constructQ1_9_5Builder() {

- return new NodeMetadataBuilder(NodeIDString.Q1_9_5)

- .setBuilderUserText(UiTextConstant.Q1_9_5_TEXT)

- .buildNodeTypeAsOtherIa();

- }

-

- private static NodeMetadataBuilder constructQ1_9Builder(List<NodeMetadata> dependencies) {

- Node node = new Node(NodeIDString.C1_9, dependencies);

- return new NodeMetadataBuilder(node)

- .setBuilderUserText(UiTextConstant.C1_9_TEXT)

- .addScalarRelation(new RescheduledEventsRelation(node));

- }

-

- private static NodeMetadataBuilder constructC1_10Builder(List<NodeMetadata> dependencies) {

- Node node = new Node(NodeIDString.C1_10, dependencies);

- return new NodeMetadataBuilder(node)

- .setBuilderUserText(UiTextConstant.C1_10_TEXT)

- .addScalarRelation(new DisruptionRelation(node));

- }

-}

-package org.singinst.uf.model;

-

-import java.util.List;

-

-public class NormalConstraint {

- private final double lowerBound;

- private final List<ScalarSchema> scalarSchemata;

- private boolean disable;

-

- public NormalConstraint(List<ScalarSchema> scalarSchemata, double lowerBound) {

- this.scalarSchemata = scalarSchemata;

- this.lowerBound = lowerBound;

- }

-

- private final ValueListener leftAnchorListener = new MooreUpdateListener() {

- void adjust() {

- getRightConjecture().setValue(extrapolate(getLeftConjecture(), getMiddleConjecture()));

- }

- };

-

- private final ValueListener rightAnchorListener = new MooreUpdateListener() {

- void adjust() {

- getLeftConjecture().setValue(extrapolate(getRightConjecture(), getMiddleConjecture()));

- }

- };

-

- public void constrain() {

- getLeftConjecture().addUpdateListener(leftAnchorListener);

- getMiddleConjecture().addUpdateListener(leftAnchorListener);

- getRightConjecture().addUpdateListener(rightAnchorListener);

- }

-

- protected double extrapolate(ScalarValueHolder firstConjecture,

- ScalarValueHolder secondConjecture) {

- return secondConjecture.getValue() * 2 - firstConjecture.getValue();

- }

-

- private ScalarValueHolder getConjecture(int i) {

- return scalarSchemata.get(i).getScalarValueHolder();

- }

-

- private ScalarValueHolder getLeftConjecture() {

- return getConjecture(0);

- }

-

- private ScalarValueHolder getMiddleConjecture() {

- return getConjecture(1);

- }

-

- private ScalarValueHolder getRightConjecture() {

- return getConjecture(2);

- }

-

- private abstract class MooreUpdateListener implements ValueListener {

-

- public void fireUpdate(double value) {

- if (!disable) {

- disable = true;

- try {

- adjust();

- fixLeftToRight();

- } finally {

- disable = false;

- }

- }

- }

-

- abstract void adjust();

-

- private void fixLeftToRight() {

- double left = getLeftConjecture().getValue();

- double right = getRightConjecture().getValue();

- if (left > right) {

- getLeftConjecture().setValue(right);

- getRightConjecture().setValue(left);

- }

- if (getLeftConjecture().getValue() < lowerBound) {

- if (getMiddleConjecture().getValue() < lowerBound) {

- getMiddleConjecture().setValue(lowerBound);

- }

- getLeftConjecture().setValue(lowerBound);

- getRightConjecture().setValue(extrapolate(getLeftConjecture(), getMiddleConjecture()));

- }

- }

-

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Arrays;

-import java.util.Collection;

-import java.util.Collections;

-import java.util.Iterator;

-import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.presenter.ClickableCurve;

-import org.singinst.uf.presenter.HtmlUtil;

-import org.singinst.uf.presenter.ScalarValuePointList;

-import org.singinst.uf.presenter.LineBounds;

-import org.singinst.uf.presenter.MouseClickListener;

-import org.singinst.uf.presenter.SimpleLine;

-import org.singinst.uf.presenter.SimpleStyle;

-

-public class NormalNodeMetadataContentsFactory implements MouseClickListener {

-

- private final Collection<ScalarSchema> scalarSchemata = new ArrayList<ScalarSchema>();

- private final List<ScalarValueHolder> scalarValueHolders = new ArrayList<ScalarValueHolder>();

-

- private final ScalarValuePointList curvePointList = new ClickableCurve(this);

- private final ScalarValuePointList pointList5 = new ScalarValuePointList(new SimpleStyle(SimpleColor.GREEN));

- private final ScalarValuePointList pointList50 = new ScalarValuePointList(new SimpleStyle(SimpleColor.BLACK));

- private final ScalarValuePointList pointList95 = new ScalarValuePointList(new SimpleStyle(SimpleColor.RED));

-

- private final ScalarRelation relation;

- private final ScalarSchema mean;

- //private final ScalarSchema median;

- private final ScalarSchema stdDev;

- private final String normalUnits;

-

- public NormalNodeMetadataContentsFactory(Node node, String units, final LineBounds meanBounds, String stdSuffix) {

- String powerStr = meanBounds.displayAsLog() ? " 10 to the " : " ";

- // if we are not displaying as log, it is going to be 'year', so add an 's'

- String plural = meanBounds.displayAsLog() ? "" : "s";

-

- this.normalUnits = units;

- mean = new ScalarSchema(node, ScalarSubIDString.MEAN, meanBounds, meanUnits(), "The median value is " + powerStr, plural, meanBounds, true);

- double maxStdDev = meanBounds.getLength() / 10;

- stdDev = new ScalarSchema(node, ScalarSubIDString.STD_DEV, new LineBounds(0, maxStdDev), units, "std dev is " + stdSuffix + powerStr, "", new LineBounds(0, maxStdDev * 100), true);

- Axis vertical = new Axis(new LineBounds(0, 5 / maxStdDev), false);

- Axis horizontal = new Axis(meanBounds);

- scalarSchemata.add(mean);

- scalarSchemata.add(stdDev);

- scalarValueHolders.add(mean.getScalarValueHolder());

- scalarValueHolders.add(stdDev.getScalarValueHolder());

-

- relation = new ScalarRelation(horizontal, vertical) {

-

- @Override

- public List<ScalarValuePointList> getPointLists() {

- List<SimplePoint> pointSamples = new ArrayList<SimplePoint>();

- Iterable<Double> samples =

- meanBounds.getVisualSamples(5001, mean.getScalarValueHolder().getValue(), Arrays.asList(getPercentile5(), getPercentile95()));

- for (double x : samples) {

- pointSamples.add(new SimplePoint(x, yFromX(x)));

- }

- curvePointList.setHypothesisPoints(pointSamples);

-

- pointList5.setHypothesisPoints(verticalLine(getPercentile5()));

- pointList50.setHypothesisPoints(verticalLine(mean.getScalarValueHolder().getValue()));

- pointList95.setHypothesisPoints(verticalLine(getPercentile95()));

-

- List<ScalarValuePointList> retVal = Arrays.asList(curvePointList, pointList5, pointList50, pointList95);

- for (ScalarValuePointList scalarValuePointList : retVal) {

- List<SimplePoint> filteredList = new ArrayList<SimplePoint>();

- for (SimplePoint simplePoint : scalarValuePointList.getHypothesisPoints()) {

- double x = simplePoint.x;

- if (x >= meanBounds.getFirst() && x <= meanBounds.getSecond()) {

- filteredList.add(simplePoint);

- }

- }

- scalarValuePointList.setHypothesisPoints(filteredList);

- }

- return retVal;

- }

-

- private List<SimplePoint> verticalLine(double x) {

- SimpleLine line = SimpleLine.vertical(x, 0, yFromX(x));

- return Arrays.asList(line.p1, line.p2);

- }

-

- public List<? extends ConclusionReportGenerator> getConclusionGenerators() {

- return Collections.singletonList(new ConclusionReportGenerator() {

- public String getText(ScalarValueHolder scalarValueHolder, double value) {

- boolean power = meanBounds.displayAsLog();

- // if we are not displaying as log, it is going to be 'year', so add an 's'

- String plural = power ? "" : "s";

- String str = "There is a 90% chance of being between ";

- if (power){

- str += HtmlUtil.green("10 ^{" + mean.getBounds().userFormat(getPercentile5()) + "}") +

- " and " +

- HtmlUtil.red("10 ^{" + mean.getBounds().userFormat(getPercentile95()) + "}") + " " + meanUnits() + plural;

- }

- else {

- str += HtmlUtil.green(mean.getBounds().userFormat(getPercentile5())) + " and " +

- HtmlUtil.red(mean.getBounds().userFormat(getPercentile95())) + " " + meanUnits() + plural;

- }

-

- return str;

- }

- });

- }

-

- public List<ScalarValueHolder> getScalarValues() {

- return scalarValueHolders;

- }

-

- };

- }

-

- private Double getPercentile5() {

- return mean.getScalarValueHolder().getValue() - MathUtil.NINETY_FIVE_PERCENTILE * stdDev.getScalarValueHolder().getValue();

- }

-

- private Double getPercentile95() {

- return mean.getScalarValueHolder().getValue() + MathUtil.NINETY_FIVE_PERCENTILE * stdDev.getScalarValueHolder().getValue();

- }

-

- protected double yFromX(double x) {

- double meanValue = mean.getScalarValueHolder().getValue();

- double stdDevValue = stdDev.getScalarValueHolder().getValue();

- double squaredDistanceFromMean = (meanValue - x) * (meanValue - x);

- double variance = stdDevValue * stdDevValue;

- double functionalY = (1 / Math.sqrt(2 * Math.PI * variance)) * Math.exp(-1 * squaredDistanceFromMean / (2 * variance));

- return functionalY;

- }

-

- public Collection<? extends ScalarSchema> getScalars() {

- return scalarSchemata;

- }

-

- public ScalarRelation getRelation() {

- return relation;

- }

-

- public void mouseClick(SimplePoint point) {

- double newMean = point.x;

- double newDensityAtMean = point.y;

- double newVariance = 1 / (2 * Math.PI * newDensityAtMean * newDensityAtMean);

- stdDev.getScalarValueHolder().setValue(Math.sqrt(newVariance));

- mean.getScalarValueHolder().setValue(newMean);

-

- }

-

- // TODO5 called from constructor

- protected String meanUnits() {

- return normalUnits;

- }

-}

-package org.singinst.uf.model;

-

-import org.singinst.uf.math.MathUtil;

-

-public enum NotablePercentile {

- PERCENTILE5(5, SimpleColor.GREEN, 1, -1 * MathUtil.NINETY_FIVE_PERCENTILE),

- PERCENTILE50(50, SimpleColor.BLACK, 2, 0),

- PERCENTILE95(95, SimpleColor.RED, 1, MathUtil.NINETY_FIVE_PERCENTILE);

-

- private final int value;

- private final SimpleColor color;

- private final int priority;

- private final double offset;

-

- private NotablePercentile(int value, SimpleColor color, int priority, double offset) {

- this.value = value;

- this.color = color;

- this.priority = priority;

- this.offset = offset;

- }

-

- public int getPriority() {

- return priority;

- }

-

- public int getValue() {

- return value;

- }

-

- public SimpleColor getColor() {

- return color;

- }

-

- public double getOffset() {

- return offset;

- }

-

- /**

- * Returns a human readable description of whether or not this is extremely high, mid or extremely low probability.

- *

- * @param adjectiveSmall

- * The adjective to use if it is astonishingly (blank) (5th percentile)

- * @param adjectiveLarge

- * The adjective to use if it is astonishingly (blank) (95th percentile)

- * @return

- * String describing the range this NotablePercentile covers.

- */

- public String getText(String adjectiveSmall, String adjectiveLarge) {

- switch (value) {

- case 5: return "Astonishingly " + adjectiveSmall;// + " (5th percentile)";

- case 50: return "Median";

- case 95: return "Astonishingly " + adjectiveLarge;// + " (95th percentile)";

- default: return "";

- }

- }

-}

-package org.singinst.uf.model;

-

-import org.singinst.uf.presenter.HtmlUtil;

-import org.singinst.uf.presenter.LineBounds;

-import org.singinst.uf.presenter.NumericEntry;

-

-public class OtherIaExtreme extends YearExtremeNodeMetadataContentsFactory {

-

- protected OtherIaExtreme(Node node, LineBounds rangeBounds,

- double year) {

- super(node, rangeBounds, year, " log speedup in year " + (int) year , "", "low", "high");

- }

-

- @Override

- public ConclusionReportGenerator getConclusionGenerator() {

- return new ConclusionReportGenerator() {

- public String getText(ScalarValueHolder scalarValueHolder, double value) {

- return "The research speedup in the year " + getYearString() + " has a 90% chance of being between " +

- HtmlUtil.green(display(getScalars().get(0).getScalarValueHolder().getValue())) +

- " and " + HtmlUtil.red(display(getScalars().get(2).getScalarValueHolder().getValue()));

- }

-

- private String display(double value) {

- return NumericEntry.formatAsScalar(Math.pow(10, value)) + "x";

- }

- };

-

- }

-

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Collection;

-import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.presenter.ScalarValuePointList;

-import org.singinst.uf.presenter.LineBounds;

-

-public class OtherIaNodeMetadataContentsFactory {

-

- private final YearExtremeNodeMetadataContentsFactory extreme;

- private final List<ScalarSchema> scalarSchemata = new ArrayList<ScalarSchema>();

- private final ScalarRelation relation;

-

- private final List<PercentileDraggableLine> pointLists = new ArrayList<PercentileDraggableLine>();

-

- private final List<ScalarValueHolder> conjectureList = new ArrayList<ScalarValueHolder>();

-

-

- public OtherIaNodeMetadataContentsFactory(Node node) {

- final LineBounds range = new LineBounds(0, 2, true);

- final LineBounds domain = new LineBounds(ModelUtil.EARLIEST_YEAR, ModelUtil.LATEST_YEAR);

- extreme = new OtherIaExtreme(node, range, ModelUtil.LATEST_YEAR);

- scalarSchemata.addAll(extreme.getScalars());

- for (ScalarSchema scalarSchema : scalarSchemata) {

- conjectureList.add(scalarSchema.getScalarValueHolder());

- }

-

- for (final NotablePercentile percentile : NotablePercentile.values()) {

- PercentileDraggableLine pointList = new PercentileDraggableLine(percentile) {

-

- @Override

- public void dragTo(SimplePoint point) {

- OtherIaNodeMetadataContentsFactory.this.dragTo(percentile, point);

- }};

- pointLists.add(pointList);

- }

-

- relation = new ScalarRelation(new Axis(domain), new Axis(range)) {

-

- @Override

- public List<? extends ScalarValuePointList> getPointLists() {

- for (PercentileDraggableLine incidentLine : pointLists) {

- List<SimplePoint> points = new ArrayList<SimplePoint>();

- points.add(new SimplePoint(domain.getFirst(), range.getFirst()));

- points.add(new SimplePoint(extreme.getYear(),

- extreme.getScalarSchema(incidentLine.getPercentile()).getScalarValueHolder().getValue()));

- incidentLine.setHypothesisPoints(points);

- }

- return pointLists;

- }

-

- public List<? extends ConclusionReportGenerator> getConclusionGenerators() {

- List<ConclusionReportGenerator> retVal = new ArrayList<ConclusionReportGenerator>();

- retVal.add(extreme.getConclusionGenerator());

- return retVal;

- }

-

- public List<ScalarValueHolder> getScalarValues() {

- return conjectureList ;

- }

- };

- }

-

- public Collection<? extends ScalarSchema> getScalars() {

- return scalarSchemata;

- }

-

- public ScalarRelation getRelation() {

- return relation;

- }

-

- public void dragTo(NotablePercentile percentile, SimplePoint point) {

- SimplePoint anchorPoint = new SimplePoint(ModelUtil.EARLIEST_YEAR, 0);

- double newExtremeY = MathUtil.interpolate(

- anchorPoint, point, ModelUtil.LATEST_YEAR);

- extreme.getScalarSchema(percentile).getScalarValueHolder().setValue(newExtremeY);

- }

-}

-package org.singinst.uf.model;

-

-import org.singinst.uf.presenter.DraggableLine;

-import org.singinst.uf.presenter.SimpleStyle;

-

-public abstract class PercentileDraggableLine extends DraggableLine {

-

- private final NotablePercentile percentile;

-

- public PercentileDraggableLine(NotablePercentile percentile) {

- super(percentile.getPriority(), new SimpleStyle(percentile.getColor()));

- this.percentile = percentile;

- }

-

- public NotablePercentile getPercentile() {

- return percentile;

- }

-

-}

-package org.singinst.uf.model;

-

-import org.singinst.uf.math.InvertableFunction;

-

-public class PowerFunction extends InvertableFunction {

-

- private final double zero;

-

- public PowerFunction(double zero) {

- this.zero = zero;

- }

-

- @Override

- public double apply(double x) {

- return Math.pow(10, x) + zero;

- }

-

- @Override

- public double invert(double y) {

- return Math.log10(y - zero);

- }

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.List;

-import java.util.TreeSet;

-

-import org.singinst.uf.math.MathUtil;

-

-public class RescheduledEventsRelation extends CalculationRelation {

-

- /*@Override

- protected Calculation getCalculationForYear(double year) {

- // TODO Auto-generated method stub

- return null;

- }*/

-

- public RescheduledEventsRelation(Node node) {

- super(node);

- }

-

-

- protected Calculation probabilityAiIsPossible() {

- return new Calculation("Probability that AGI is possible") {

- @Override

- protected double rawEvaluate(StringBuilder htmlConsole) {

- MathUtil.ScienceSpeedModelParameters scienceSpeedModelParameters = scienceSpeedModel();

- org.singinst.uf.common.LogUtil.info(String.format(

- "institutional_base_year: %g\n"

- + "institutional_mean_slope_log_factor: %g\n"

- + "institutional_stddev_slope_log_factor: %g\n"

- + "population_base_year: %g\n"

- + "population_mean_log_year: %g\n"

- + "population_stddev_log_year: %g\n"

- + "population_mean_init_log_rate: %g\n"

- + "population_stddev_init_log_rate: %g\n"

- + "population_mean_slope_log_rate: %g\n"

- + "population_stddev_slope_log_rate: %g\n"

- + "sequencedata_base_year: %g\n"

- + "sequencedata_mean_log_year: %g\n"

- + "sequencedata_stddev_log_year: %g\n"

- + "sequencedata_mean_init_log_factor: %g\n"

- + "sequencedata_stddev_init_log_factor: %g\n"

- + "sequencedata_mean_slope_log_factor: %g\n"

- + "sequencedata_stddev_slope_log_factor: %g\n"

- , scienceSpeedModelParameters.institutional_base_year

- , scienceSpeedModelParameters.institutional_mean_slope_log_factor

- , scienceSpeedModelParameters.institutional_stddev_slope_log_factor

- , scienceSpeedModelParameters.population_base_year

- , scienceSpeedModelParameters.population_mean_log_year

- , scienceSpeedModelParameters.population_stddev_log_year

- , scienceSpeedModelParameters.population_mean_init_log_rate

- , scienceSpeedModelParameters.population_stddev_init_log_rate

- , scienceSpeedModelParameters.population_mean_slope_log_rate

- , scienceSpeedModelParameters.population_stddev_slope_log_rate

- , scienceSpeedModelParameters.sequencedata_base_year

- , scienceSpeedModelParameters.sequencedata_mean_log_year

- , scienceSpeedModelParameters.sequencedata_stddev_log_year

- , scienceSpeedModelParameters.sequencedata_mean_init_log_factor

- , scienceSpeedModelParameters.sequencedata_stddev_init_log_factor

- , scienceSpeedModelParameters.sequencedata_mean_slope_log_factor

- , scienceSpeedModelParameters.sequencedata_stddev_slope_log_factor

- ));

- return getDependency().value(NodeIDString.Q1_1, ScalarSubIDString.PROBABILITY) / 100;

- }

- };

- }

-

- MathUtil.EventDiscreteDistributionSchedule rescheduledProbabilities;

-

- @Override

- protected List<Calculation> getCalculations(final List<Double> years) {

- List<Calculation> retVal = new ArrayList<Calculation>();

- for (final double year : years) {

- retVal.add(new Calculation("Probability of AI by " + (int) year) {

-

- @Override

- protected double rawEvaluate(StringBuilder htmlConsole) {

- if (year == ModelUtil.BASIC_MODEL_YEARS[0]) {

- calculate(MathUtil.wrapDoubleArray(ModelUtil.BASIC_MODEL_YEARS));

- }

- return MathUtil.expc(rescheduledProbabilities.logitnerp(year).clogProbEvent);

- }

- });

- }

- return retVal;

- }

-

- private void calculate(List<Double> years) {

- double[] yearsInOtherCalculations = ModelUtil.BASIC_MODEL_YEARS;

- double[] yearsHere = MathUtil.unwrapDoubleList(years);

- //new double[years.size()];

- //for (int i=0; i<yearsHere.length; ++i)

- // yearsHere[i] = years.get(i);

-

- TreeSet<Double> beforeYearsSortedSet = new TreeSet<Double>(MathUtil.wrapDoubleArray(yearsInOtherCalculations));

- {

- double i = ModelUtil.LATEST_YEAR - ModelUtil.EARLIEST_IA_YEAR;

- double factor =

- (yearsInOtherCalculations[yearsInOtherCalculations.length-1] - ModelUtil.EARLIEST_IA_YEAR)

- / (yearsInOtherCalculations[yearsInOtherCalculations.length-2] - ModelUtil.EARLIEST_IA_YEAR)

- ;

- i *= factor;

- while (i <= ModelUtil.LATEST_UNACCELERATED_YEAR - ModelUtil.EARLIEST_IA_YEAR) {

- beforeYearsSortedSet.add(Math.round(i/ModelUtil.YEAR_STEPSIZE)*ModelUtil.YEAR_STEPSIZE + ModelUtil.EARLIEST_IA_YEAR);

- i *= factor;

- };

- i = ModelUtil.LATEST_YEAR - ModelUtil.EARLIEST_IA_YEAR;

- i /= factor;

- while (i <= ModelUtil.EARLIEST_YEAR - ModelUtil.EARLIEST_IA_YEAR) {

- beforeYearsSortedSet.add(Math.round(i/ModelUtil.YEAR_STEPSIZE)*ModelUtil.YEAR_STEPSIZE + ModelUtil.EARLIEST_IA_YEAR);

- i /= factor;

- };

- }

-

- double[] beforeYears = MathUtil.unwrapDoubleList(new ArrayList<Double>(beforeYearsSortedSet));

-

- //List<Double> clogPAiWrapped;

-

- double[] clogPAi;

- //clogPAi = MathUtil.unwrapDoubleList(clogPAiWrapped);

- clogPAi = new double[beforeYears.length];

- for (int i=0; i<clogPAi.length; ++i) {

- // clogPAI[i] = 100;

- clogPAi[i] = MathUtil.clog(ScalarValueHolder.findById(NodeIDString.C1_6, ScalarSubIDString.yearProbabilityID(beforeYears[i])).getValue());

- if (clogPAi[i]>300d) clogPAi[i]=300d;

- }

- MathUtil.EventDiscreteDistributionSchedule s_before = new MathUtil.EventDiscreteDistributionSchedule();

- MathUtil.EventDiscreteDistributionSchedule s_after;

-

- s_before.time = beforeYears; //ModelUtil.BASIC_MODEL_YEARS;

- //s_before.time = java.util.Arrays.copyOf(ModelUtil.BASIC_MODEL_YEARS, yearsInOtherCalculations.length+2);

- //s_before.time[yearsInOtherCalculations.length] = ModelUtil.LATEST_YEAR*(1+2*MathUtil.DOUBLE_EPS);

- //s_before.time[yearsInOtherCalculations.length+1] = ModelUtil.LATEST_YEAR*(1+4*MathUtil.DOUBLE_EPS);

- s_before.clogProbEvent = clogPAi;

- //s_before.clogProbEvent = java.util.Arrays.copyOf(clogPAI, yearsInOtherCalculations.length+2);

- s_before.logitSubevents = new double[][]{};

-

- MathUtil.ScienceSpeedModelParameters scienceSpeedModelParameters = scienceSpeedModel();

-

-

- s_after = MathUtil.clogMarginalScienceSpeedEventRescheduling(

- scienceSpeedModelParameters,

- s_before, yearsHere, 1000);

-

- rescheduledProbabilities = s_after;

-

- //List<Double> rescheduledProbabilities = new java.util.ArrayList<Double>(years.size());

- //for (int i=0; i<rescheduledProbabilities.size(); ++i) {

- // rescheduledProbabilities.set(i, MathUtil.expc(s_after.clogProbEvent[i]));

- //}

-

- /*

- yearsInOtherCalculations = new double[ModelUtil.]*/

-

- //return rescheduledProbabilities;

-

- }

-

- // glue equations

- private MathUtil.ScienceSpeedModelParameters scienceSpeedModel() {

- MathUtil.ScienceSpeedModelParameters o = new MathUtil.ScienceSpeedModelParameters();

-

- o.sequencedata_base_year = ModelUtil.ANCHOR_FAR_YEAR + ModelUtil.RESEARCH_CAREER_DELAY;

- o.sequencedata_mean_log_year = getDependency().value(NodeIDString.Q1_9_1, ScalarSubIDString.MEAN)*Math.log(10);

- o.sequencedata_stddev_log_year = getDependency().value(NodeIDString.Q1_9_1, ScalarSubIDString.STD_DEV)*Math.log(10);

-

- o.population_base_year = ModelUtil.ANCHOR_FAR_YEAR + ModelUtil.RESEARCH_CAREER_DELAY;

- o.population_mean_log_year = getDependency().value(NodeIDString.Q1_9_2, ScalarSubIDString.MEAN)*Math.log(10);

- o.population_stddev_log_year = getDependency().value(NodeIDString.Q1_9_2, ScalarSubIDString.STD_DEV)*Math.log(10);

-

- double t0, t1;

- t0 = 0; // EARLIEST_IA_DELAY

- t1 = ModelUtil.LATEST_IA_DELAY;

- double klow, khigh;

- klow = NotablePercentile.PERCENTILE5.getValue();

- khigh = NotablePercentile.PERCENTILE95.getValue();

- double zlow, zhigh;

- zlow = NotablePercentile.PERCENTILE5.getOffset();

- zhigh = NotablePercentile.PERCENTILE95.getOffset();

- double y0low, y0high, y1low, y1high;

- double mean_init, stddev_init, mean_slope, stddev_slope, mean_final, stddev_final;

- //y0low = getDependency().value(NodeIDString.Q1_9_3, ScalarSubIDString.yearPercentileID(t0, klow ))*Math.log(10);

- //y0high = getDependency().value(NodeIDString.Q1_9_3, ScalarSubIDString.yearPercentileID(t0, khigh))*Math.log(10);

- y0low = 0;

- y0high = 0;

- y1low = getDependency().value(NodeIDString.Q1_9_3, ScalarSubIDString.yearPercentileID(t1, klow ))*Math.log(10);

- y1high = getDependency().value(NodeIDString.Q1_9_3, ScalarSubIDString.yearPercentileID(t1, khigh))*Math.log(10);

- mean_init = MathUtil.linterp(zlow, zhigh, y0low, y0high, 0);

- stddev_init = (y0high-y0low)/(zhigh-zlow);

- mean_final = MathUtil.linterp(zlow, zhigh, y1low, y1high, 0);

- stddev_final = (y1high-y1low)/(zhigh-zlow);

- mean_slope = (mean_final-mean_init)/(t1-t0);

- stddev_slope = (stddev_final-stddev_init)/(t1-t0);

- o.sequencedata_mean_init_log_factor = mean_init;

- o.sequencedata_mean_slope_log_factor = mean_slope;

- o.sequencedata_stddev_init_log_factor = stddev_init;

- o.sequencedata_stddev_slope_log_factor = stddev_slope;

-

- t0 = 0; // EARLIEST_IA_DELAY

- t1 = ModelUtil.LATEST_IA_DELAY;

- y0low = getDependency().value(NodeIDString.Q1_9_4, ScalarSubIDString.yearPercentileID(t0, klow ))*Math.log(10);

- y0high = getDependency().value(NodeIDString.Q1_9_4, ScalarSubIDString.yearPercentileID(t0, khigh))*Math.log(10);

- y1low = getDependency().value(NodeIDString.Q1_9_4, ScalarSubIDString.yearPercentileID(t1, klow ))*Math.log(10);

- y1high = getDependency().value(NodeIDString.Q1_9_4, ScalarSubIDString.yearPercentileID(t1, khigh))*Math.log(10);

- mean_init = MathUtil.linterp(zlow, zhigh, y0low, y0high, 0);

- stddev_init = (y0high-y0low)/(zhigh-zlow);

- mean_final = MathUtil.linterp(zlow, zhigh, y1low, y1high, 0);

- stddev_final = (y1high-y1low)/(zhigh-zlow);

- mean_slope = (mean_final-mean_init)/(t1-t0);

- stddev_slope = (stddev_final-stddev_init)/(t1-t0);

- o.population_mean_init_log_rate = mean_init - Math.log(ModelUtil.UNENHANCED_RESEARCHERS);

- o.population_mean_slope_log_rate = mean_slope - Math.log(ModelUtil.UNENHANCED_RESEARCHERS);

- o.population_stddev_init_log_rate = stddev_init - Math.log(ModelUtil.UNENHANCED_RESEARCHERS);

- o.population_stddev_slope_log_rate = stddev_slope - Math.log(ModelUtil.UNENHANCED_RESEARCHERS);

-

- t0 = ModelUtil.EARLIEST_YEAR;

- t1 = ModelUtil.LATEST_YEAR;

- y0low = 0;

- y0high = 0;

- y1low = getDependency().value(NodeIDString.Q1_9_5, ScalarSubIDString.yearPercentileID(t1, klow ))*Math.log(10);

- y1high = getDependency().value(NodeIDString.Q1_9_5, ScalarSubIDString.yearPercentileID(t1, khigh))*Math.log(10);

- mean_init = MathUtil.linterp(zlow, zhigh, y0low, y0high, 0);

- stddev_init = (y0high-y0low)/(zhigh-zlow);

- mean_final = MathUtil.linterp(zlow, zhigh, y1low, y1high, 0);

- stddev_final = (y1high-y1low)/(zhigh-zlow);

- mean_slope = (mean_final-mean_init)/(t1-t0);

- stddev_slope = (stddev_final-stddev_init)/(t1-t0);

- o.institutional_base_year = ModelUtil.EARLIEST_YEAR;

- o.institutional_mean_slope_log_factor = mean_slope;

- o.institutional_stddev_slope_log_factor = stddev_slope;

-

- return o;

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.Arrays;

-import java.util.List;

-

-import org.singinst.uf.presenter.HtmlUtil;

-import org.singinst.uf.presenter.LineBounds;

-

-public class ResearcherExtreme extends YearExtremeNodeMetadataContentsFactory {

-

-

- protected ResearcherExtreme(Node node, LineBounds rangeBounds,

- double year) {

- super(node, rangeBounds, year, " log #, " + (int) year + " years later", "", "low", "high");

- }

-

- public static List<ResearcherExtreme> createList(Node node, LineBounds rangeBounds, LineBounds yearBounds) {

- ResearcherExtreme incidentEarly = new ResearcherExtreme(node, rangeBounds, yearBounds.getFirst());

- ResearcherExtreme incidentLate = new ResearcherExtreme(node, rangeBounds, yearBounds.getSecond());

- return Arrays.asList(incidentEarly, incidentLate);

- }

-

- @Override

- public ConclusionReportGenerator getConclusionGenerator() {

- return new ConclusionReportGenerator() {

- public String getText(ScalarValueHolder scalarValueHolder, double value) {

- return "The number of IA enhanced research scientists " + getYearString() + " years later " +

- "has a 90% chance of being between " +

- HtmlUtil.green(display(getScalars().get(0).getScalarValueHolder().getValue())) +

- " and " + HtmlUtil.red(display(getScalars().get(2).getScalarValueHolder().getValue()));

- }

-

- private String display(double value) {

- return "" + (int) (Math.pow(10, value));

- }

- };

-

- }

-

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Collection;

-import java.util.HashMap;

-import java.util.List;

-import java.util.Map;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.presenter.ScalarValuePointList;

-import org.singinst.uf.presenter.LineBounds;

-

-public class ResearchersNodeMetadataContentsFactory {

-

- private final List<ScalarSchema> scalarSchemata = new ArrayList<ScalarSchema>();

- private final ScalarRelation relation;

- private final Map<Double, YearExtremeNodeMetadataContentsFactory> extremeByYear = new HashMap<Double, YearExtremeNodeMetadataContentsFactory>();

-

- private final List<PercentileDraggableLine> pointLists = new ArrayList<PercentileDraggableLine>();

-

- private final List<ScalarValueHolder> conjectureList = new ArrayList<ScalarValueHolder>();

- private final LineBounds domain;

-

- public ResearchersNodeMetadataContentsFactory(Node node) {

- final LineBounds range = new LineBounds(1, 7, true);

- domain = new LineBounds(0, ModelUtil.LATEST_IA_DELAY);

- final List<ResearcherExtreme> extremes = ResearcherExtreme.createList(node, range, domain);

- for (YearExtremeNodeMetadataContentsFactory extreme : extremes) {

- extremeByYear.put(extreme.getYear(), extreme);

- scalarSchemata.addAll(extreme.getScalars());

- }

- for (ScalarSchema scalarSchema : scalarSchemata) {

- conjectureList.add(scalarSchema.getScalarValueHolder());

- }

-

- for (final NotablePercentile percentile : NotablePercentile.values()) {

- PercentileDraggableLine pointList = new PercentileDraggableLine(percentile) {

-

- @Override

- public void dragTo(SimplePoint point) {

- ResearchersNodeMetadataContentsFactory.this.dragTo(percentile, point);

- }};

- pointLists.add(pointList);

-

- }

- relation = new ScalarRelation(new Axis(domain), new Axis(range)) {

-

- @Override

- public List<? extends ScalarValuePointList> getPointLists() {

- for (PercentileDraggableLine incidentLine : pointLists) {

- List<SimplePoint> points = new ArrayList<SimplePoint>();

- for (YearExtremeNodeMetadataContentsFactory extreme : extremes) {

- points.add(new SimplePoint(extreme.getYear(),

- extreme.getScalarSchema(incidentLine.getPercentile()).getScalarValueHolder().getValue()));

- }

- incidentLine.setHypothesisPoints(points);

- }

- return pointLists;

- }

-

- public List<? extends ConclusionReportGenerator> getConclusionGenerators() {

- List<ConclusionReportGenerator> retVal = new ArrayList<ConclusionReportGenerator>();

- for (YearExtremeNodeMetadataContentsFactory extreme : extremes) {

- retVal.add(extreme.getConclusionGenerator());

- }

- return retVal;

- }

-

- public List<ScalarValueHolder> getScalarValues() {

- return conjectureList ;

- }

- };

- }

-

- public Collection<? extends ScalarSchema> getScalars() {

- return scalarSchemata;

- }

-

- public ScalarRelation getRelation() {

- return relation;

- }

-

- public void dragTo(NotablePercentile percentile, SimplePoint point) {

- boolean selectLeft = point.x < domain.getMidpoint();

- double year = selectLeft ? domain.getFirst() : domain.getSecond();

- double anchorYear = selectLeft ? domain.getSecond() : domain.getFirst();

- YearExtremeNodeMetadataContentsFactory extreme = extremeByYear.get(year);

- if (extreme == null) {

- throw new NullPointerException("" + year);

- }

- YearExtremeNodeMetadataContentsFactory anchorExtreme = extremeByYear.get(anchorYear);

- if (anchorExtreme == null) {

- throw new NullPointerException("" + year);

- }

- SimplePoint anchorPoint = new SimplePoint(

- anchorYear, anchorExtreme.getScalarSchema(percentile).getScalarValueHolder().getValue());

- double newExtremeY = MathUtil.interpolate(

- anchorPoint, point, year);

- extreme.getScalarSchema(percentile).getScalarValueHolder().setValue(newExtremeY);

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.Collection;

-import java.util.Collections;

-import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.presenter.ScalarValuePointList;

-

-

-public abstract class ScalarRelation implements SummarySource {

-

- public ScalarRelation(Axis domain, Axis range) {

- this(domain, range, EMPTY_POINTS);

- }

-

- public ScalarRelation(Axis domain, Axis range, Collection<SimplePoint> decorationPoints) {

- this.domain = domain;

- this.range = range;

- this.decorationPoints = decorationPoints;

- }

-

- public abstract List<? extends ScalarValuePointList> getPointLists();

-

- public Axis getDomain() {

- return domain;

- }

-

- public Axis getRange() {

- return range;

- }

-

- private final Axis domain;

- private final Axis range;

- public final Collection<SimplePoint> decorationPoints;

-

- private static final Collection<SimplePoint> EMPTY_POINTS = Collections.emptyList();

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.Collection;

-import java.util.Collections;

-import java.util.List;

-import java.util.Random;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.presenter.LineBounded;

-import org.singinst.uf.presenter.LineBounds;

-

-public class ScalarSchema implements SummarySource, LineBounded {

- private final String prefix;

- private final String suffix;

-

- private final Node node;

- private final String idString;

- private final LineBounds bounds;

- private final String unit;

- private final List<ConclusionReportGenerator> conclusionReportGenerators = new ArrayList<ConclusionReportGenerator>();

- private boolean visibleAxis = true;

- private boolean visibleProperty = true;

-

- private final LineBounds boundsConstraint;

-

- public ScalarSchema(Node node, String idString, LineBounds bounds, String unit, String prefix, String suffix, LineBounds boundsConstraint, boolean visibleProperty) {

- this.node = node;

- this.idString = idString;

- this.bounds = bounds;

- this.unit = unit;

- this.prefix = prefix;

- this.suffix = suffix;

- this.boundsConstraint = boundsConstraint;

- this.visibleProperty = visibleProperty;

- }

-

- public String getPrefix() {

- return prefix;

- }

-

- public String getSuffix() {

- return suffix;

- }

-

- public String getKey() {

- return node.getIdString() + "." + idString;

- }

-

- @Override

- public String toString() {

- return getKey();

- }

-

- public List<ScalarValueHolder> getScalarValues() {

- return Collections.singletonList(getScalarValueHolder());

- }

- public ScalarValueHolder getScalarValueHolder() {

- return ScalarValueHolder.findById(this);

- }

- public List<ConclusionReportGenerator> getConclusionGenerators() {

- return conclusionReportGenerators;

- }

- public LineBounds getLineBounds() {

- return getBounds();

- }

- public LineBounds getBounds() {

- return bounds;

- }

- public boolean isVisibleAxis() {

- return visibleAxis;

- }

- public boolean displayProperty() {

- return visibleProperty;

- }

- public void setVisibleAxis(boolean visibleAxis) {

- this.visibleAxis = visibleAxis;

- }

- public Collection<NodeMetadata> getDependencies() {

- return node.getDependencies();

- }

-

- public static int getMaxDecimalDigits() {

- return 4;

- }

-

- public String getUnit() {

- return unit;

- }

-

- public LineBounds getBoundsConstraint() {

- return boundsConstraint;

- }

-

- public Object monte() {

- return MathUtil.RANDOM.nextFloat() * 100 < getScalarValueHolder().getValue();

- }

-}

-package org.singinst.uf.model;

-

-public class ScalarSubIDString {

-

- public static final String PROBABILITY = "probability";

- public static final String MEAN = "mean";

- public static final String STD_DEV = "stdDev";

- public static final String MEAN_INIT = "meanInit";

- public static final String STD_DEV_INIT = "stdDevInit";

- public static final String MEAN_SLOPE = "meanSlope";

- public static final String STD_DEV_SLOPE = "stdDevSlope";

-

- public static final String K95 = "k95";

- public static final String K50 = "k50";

- public static final String K5 = "k5";

-

- public static String yearPercentileID(double year, double percentile) {

- return "y" + year + "k" + percentile;

- }

-

- public static String yearProbabilityID(double year) {

- return "y" + year + "p";

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.HashSet;

-import java.util.Set;

-

-public class ScalarValueDependency {

- private boolean validityChecked = false;

- private Set<ScalarValueHolder> dependencies = new HashSet<ScalarValueHolder>();

-

- public void validate() {

- if (!validityChecked) {

- validityChecked = true;

- }

- }

-

- public double value(String namespace, String name) {

- ScalarValueHolder scalarValueHolder = ScalarValueHolder.findById(namespace, name);

- if (!validityChecked) {

- dependencies.add(scalarValueHolder);

- }

- return scalarValueHolder.getValue();

- }

-

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.HashMap;

-import java.util.HashSet;

-import java.util.List;

-import java.util.Map;

-import java.util.Set;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.presenter.LineBounds;

-import org.singinst.uf.presenter.Store;

-

-public class ScalarValueHolder implements Evaluable {

- @Override

- public String toString() {

- return getScalar().getKey();

- }

-

- private static final Map<String, ScalarValueHolder> store = new HashMap<String, ScalarValueHolder>();

- private final List<ValueListener> valueListeners = new ArrayList<ValueListener>();

- private double value;

- private final ScalarSchema scalarSchema;

- private double timestamp;

- private Calculation calculation;

-

- public ScalarValueHolder(ScalarSchema scalarSchema, double value) {

- this.scalarSchema = scalarSchema;

- try {

- Store s = Store.getInstance();

- Double storedValue = s.get(scalarSchema.getKey());

- if (storedValue == null) {

- this.value = value;

- } else {

- this.value = storedValue;

- }

- } catch (Exception e) {

- e.printStackTrace();

- this.value = value;

- }

- }

-

- public synchronized void setValueFromUser(double requestedValue) {

- double fromDisplay = getScalar().getBounds().toDisplay().invert(requestedValue);

- setValue(fromDisplay);

- }

-

- public synchronized void setValue(double requestedValue) {

- double constrainedValue = constrain(requestedValue);

- timestamp = System.currentTimeMillis();

- this.value = constrainedValue;

- notifyListeners();

- org.singinst.uf.common.LogUtil.info("storing with key \"" + scalarSchema.getKey() + "\" and value of "+value);

- try {

- Store s = Store.getInstance();

- s.put(scalarSchema.getKey(), value);

- } catch (Exception e) {

- e.printStackTrace();

- }

- }

-

- public void notifyListeners() {

- for (ValueListener valueListener : valueListeners) {

- valueListener.fireUpdate(value);

- }

- }

-

- private double constrain(double requestedValue) {

- double rounded = MathUtil.round(requestedValue, ScalarSchema.getMaxDecimalDigits());

- LineBounds boundsConstraint = getScalar().getBoundsConstraint();

- if (boundsConstraint != null) {

- return boundsConstraint.constrain(rounded);

- } else {

- return rounded;

- }

- }

-

- public void addUpdateListener(ValueListener valueListener) {

- valueListeners.add(valueListener);

- valueListener.fireUpdate(getCachedValue());

- }

-

- public void unshiftUpdateListener(ValueListener valueListener) {

- valueListeners.add(0, valueListener);

- valueListener.fireUpdate(getCachedValue());

- }

-

- public static ScalarValueHolder findById(ScalarSchema scalarSchema) {

- String key = scalarSchema.getKey();

- ScalarValueHolder retVal = store.get(key);

- if (retVal == null) {

- LineBounds bounds = scalarSchema.getLineBounds();

- retVal = new ScalarValueHolder(scalarSchema, bounds.getMidpoint());

- store.put(key, retVal);

- }

- return retVal;

- }

-

- public synchronized double getValue() {

- if (calculation != null && needsCalc(new NeedsCalcCache())) {

- calculation.getHtmlConsole().setLength(0);

- setValue(calculation.evaluate());

- }

- return value;

- }

-

- public synchronized double getCachedValue() {

- return value;

- }

-

- private static String constructKey(String namespace, String name) {

- return namespace + "." + name;

- }

-

- public static ScalarValueHolder findById(String namespace, String name) {

- String key = constructKey(namespace, name);

- return findById(key);

- }

-

- public static ScalarValueHolder findById(String key) {

- ScalarValueHolder scalarValueHolder = store.get(key);

- if (scalarValueHolder == null) {

- throw new NullPointerException("Key not found: " + key + "; available keys are: " + store.keySet());

- } else {

- return scalarValueHolder;

- }

- }

-

- public static Calculation getCalculation(String namespace, String name) {

- return findById(namespace, name).getCalculation();

- }

-

- public synchronized boolean needsCalc(NeedsCalcCache needsCalcCache) {

- if (getScalar().getDependencies().isEmpty()) {

- return false;

- }

- if (timestamp == 0) {

- return true;

- }

-

- Set<ScalarValueHolder> otherConjectures = new HashSet<ScalarValueHolder>();

- for (NodeMetadata nodeMetadata : getScalar().getDependencies()) {

- otherConjectures.addAll(nodeMetadata.getScalarValueHolders());

-// for (ScalarSchema otherScalar : nodeMetadata.getScalars()) {

-// otherConjectures.add(otherScalar.getScalarValueHolder());

-// }

- for (ScalarRelation relation : nodeMetadata.getScalarRelations()) {

- otherConjectures.addAll(relation.getScalarValues());

- }

- }

- for (ScalarValueHolder otherConjecture : otherConjectures) {

- if (needsCalcCache.needsCalc(otherConjecture) || timestamp < otherConjecture.timestamp) {

- return true;

- }

- }

- return false;

- }

-

- public void setCalculation(Calculation calculation) {

- this.calculation = calculation;

- }

-

- public Calculation getCalculation() {

- return calculation;

- }

-

- public double evaluate(StringBuilder htmlConsole) {

- double retVal = getValue();

- if (calculation != null) {

- htmlConsole.append(calculation.getDescription() + " = " + retVal + "<br>");

- }

- return retVal;

- }

-

- public ScalarSchema getScalar() {

- return scalarSchema;

- }

-

- public static void destroyAll() {

- for (ScalarValueHolder holder: store.values()) {

- holder.destroy();

- }

- store.clear();

- }

-

- private void destroy() {

- valueListeners.clear();

- }

-}

-package org.singinst.uf.model;

-

-public enum SimpleColor {

-

- GREEN, BLACK, RED, LIGHT_GRAY;

-

- public abstract static class Visitor<T> {

- public T visit(SimpleColor color) {

- switch(color) {

- case GREEN: return visit_GREEN();

- case BLACK: return visit_BLACK();

- case RED: return visit_RED();

- case LIGHT_GRAY: return visit_LIGHT_GRAY();

- default: throw new RuntimeException();

- }

- }

-

- public abstract T visit_GREEN();

- public abstract T visit_BLACK();

- public abstract T visit_RED();

- public abstract T visit_LIGHT_GRAY();

- }

-}

-package org.singinst.uf.model;

-

-import java.util.List;

-

-public interface SummarySource {

-

- List<? extends ConclusionReportGenerator> getConclusionGenerators();

-

- List<ScalarValueHolder> getScalarValues();

-

-}

-package org.singinst.uf.model;

-

-public class UiText {

-

- private final String question;

- private final String help;

-

- public UiText(String question, String help) {

- this.question = question;

- this.help = help;

- }

-

- public String getHelp() {

- return help;

- }

-

- public String getQuestion() {

- return question;

- }

-} \ No newline at end of file

-package org.singinst.uf.model;

-

-public interface UiTextConstant {

-

- UiText Q1_1_TEXT =

- new UiText("Is artificial intelligence possible in principle?",

-// "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\">" +

- "<html>" +

-// "<HEAD>" +

-// " <META HTTP-EQUIV=\"CONTENT-TYPE\" CONTENT=\"text/html; charset=windows-1252\">" +

-// " <TITLE>1)</TITLE>" +

-// " <META NAME=\"GENERATOR\" CONTENT=\"OpenOffice.org 3.0 (Win32)\">" +

-// " <META NAME=\"AUTHOR\" CONTENT=\"Michael Anissimov\">" +

-// " <META NAME=\"CREATED\" CONTENT=\"20081007;10570000\">" +

-// " <META NAME=\"CHANGEDBY\" CONTENT=\"Rolf Nelson\">" +

-// " <META NAME=\"CHANGED\" CONTENT=\"20090118;21592300\">" +

-// " <META NAME=\"CHANGEDBY\" CONTENT=\"Rolf Nelson\">" +

-// " <STYLE TYPE=\"text/css\">" +

-// " <!--" +

-// " @page { margin-left: 0.79in; margin-right: 1.25in; margin-top: 1in; margin-bottom: 1in }" +

-// " P { margin-bottom: 0.08in; direction: ltr; color: #000000; widows: 2; orphans: 2 }" +

-// " P.western { font-family: \"Times New Roman\", serif; font-size: 12pt; so-language: en-US }" +

-// " P.cjk { font-family: \"Times New Roman\", serif; font-size: 12pt }" +

-// " P.ctl { font-family: \"Times New Roman\", serif; font-size: 12pt; so-language: ar-SA }" +

-// " A:link { color: #0000ff }" +

-// " -->" +

-// " </STYLE>" +

-// "</HEAD>" +

- "<BODY LANG=\"en-US\" TEXT=\"#000000\" LINK=\"#0000ff\" DIR=\"LTR\">" +

- "<P CLASS=\"western\" STYLE=\"margin-bottom: 0in\">Gene sequencing costs" +

- "have been dropping exponentially for some time. Here is a graph (from" +

- "Kurzweil 2005) that shows the trend from 1990 to 2004:</P>" +

- "<P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><IMG SRC=\"data:image/gif;base64,R0lGODlhEAAOALMAAOazToeHh0tLS/7LZv/0jvb29t/f3//Ub//ge8WSLf/rhf/3kdbW1mxsbP//mf///yH5BAAAAAAALAAAAAAQAA4AAARe8L1Ekyky67QZ1hLnjM5UUde0ECwLJoExKcpp\n" +

- "V0aCcGCmTIHEIUEqjgaORCMxIC6e0CcguWw6aFjsVMkkIr7g77ZKPJjPZqIyd7sJAgVGoEGv2xsBxqNgYPj/gAwXEQA7\" " +

- "ALIGN=BOTTOM WIDTH=500 HEIGHT=383 BORDER=0></P>" +

- "<P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><BR>Germline genetic" +

- "modification for enhancing human intelligence would depend upon the" +

- "availability and cost of sequenced genomes. When will it be possible" +

- "to sequence an entire human genome for less than $1000?</P>" +

- "<P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><BR>" +

- "</P>" +

- "<UL>" +

- " <LI><P CLASS=\"western\" STYLE=\"margin-top: 0.19in; margin-bottom: 0in\">" +

- " <B>Claim: </B>According to the trendline in the Kurzweil graph," +

- " assuming a sequencing cost of $0.002 per base pair in 2008 (based on" +

- " a $60,000 full-genome sequence by Applied Biosystems in March 2008)" +

- " and a cost halving time of 1.9 years, we can assume $1,000 genomes" +

- " sometime around 2020.<BR><B>Implication: </B>$1,000 genomes in" +

- " 2020.<BR><B>Source: </B>Kurzweil, Ray. <I>The Singularity is Near:" +

- " When Humans Transcend Biology</I>. 2005. New York: Penguin" +

- " (Non-Classics).</P>" +

- " <LI><P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><B>Claim:</B> The" +

- " first human genome cost either $3 billion (Human Genome Project) or" +

- " $300 million (Celera Genomics) in 2001, James Watson&rsquo;s genome" +

- " cost $2 million in 2007, and Applied Biosystems sequenced a human" +

- " genome for $60,000 in two weeks in March 2008. One company," +

- " Intelligent Bio-systems, has claimed they will be able to provide" +

- " $5,000, 24-hour genomes by the end of 2008. <BR><B>Implication: </B>The" +

- " number of human genomes we can sequence for a fixed cost has been" +

- " increasing at a sharp exponential, often exceeding earlier" +

- " expectations.<BR><B>Sources:</B> Chu, Wai Lang. &quot;Applied Bio" +

- " sequences a human genome for $60,000.&quot;&nbsp;" +

- " LabTechnologist.com. 18 Mar. 2008. 9 Aug. 2008" +

- " &lt;<A HREF=\"http://www.labtechnologist.com/products/applied-bio-sequences-a-human-genome-for-60-000\" TARGET=\"_blank\"><FONT COLOR=\"#0000ff\"><U>http://www.labtechnologist.com/products/applied-bio-sequences-a-human-genome-for-60-000</U></FONT></A>&gt;.&nbsp;<BR>Singer," +

- " Emily. &quot;The $2 Million Genome.&quot; Technology Review. 1" +

- " June&nbsp;<BR>2007. Massachusetts Institute of Technology. 9 Aug." +

- " 2008&nbsp;<BR>&lt;<A HREF=\"http://www.technologyreview.com/biotech/18809/\" TARGET=\"_blank\"><FONT COLOR=\"#0000ff\"><U>http://www.technologyreview.com/biotech/18809/</U></FONT></A>&gt;.&nbsp;<BR>Wang," +

- " Brian. &quot;Whole genome sequencing costs continue to fall:" +

- " $300&nbsp;<BR>million in 2003, $1 million 2007, $60,000 now, $5000" +

- " by year end.&quot;&nbsp;<BR>Next Big Future. 25 Mar. 2008. 9 Aug." +

- " 2008&nbsp;<BR>&lt;<A HREF=\"http://nextbigfuture.com/2008/03/genome-sequencing-costs-continue-to.html\" TARGET=\"_blank\"><FONT COLOR=\"#0000ff\"><U>http://nextbigfuture.com/2008/03/genome-sequencing-costs-continue-to.html</U></FONT></A>&gt;.&nbsp;</P>" +

- "</UL>" +

- "<UL>" +

- " <LI><P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><B>Claim:</B> The" +

- " cost of gene sequencing (per base pair) has been decreasing" +

- " exponentially since 1970.<BR><B>Implication:</B> Unless something" +

- " changes, exponential growth in capability will probably continue" +

- " into the future.<BR><B>Source:</B> Shendure, Jay, Robi D. Mitra," +

- " Chris Varma, and George M. Church.&nbsp;<BR>&quot;Advanced" +

- " Sequencing Technologies: Methods and Goals.&quot; Nature" +

- " Reviews&nbsp;<BR>Genetics 5 (2004): 335-44. 9 Aug. 2008" +

- " &lt;<A HREF=\"http://www.nature.com/nrg/journal/v5/n5/abs/nrg1325_fs.html\" TARGET=\"_blank\"><FONT COLOR=\"#0000ff\"><U>http://www.nature.com/nrg/journal/v5/n5/abs/nrg1325_fs.html</U></FONT></A>&gt;.</P>" +

- "</UL>" +

- "<P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><BR>" +

- "</P>" +

- "<UL>" +

- " <LI><P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><B>Claim:</B> The" +

- " Archon X prize, for rapid sequencing of 100 human genomes at less" +

- " than $10,000 each, has a deadline of October 2013.<BR><B>Implication:</B>" +

- " Some scientists think that DNA sequencing costs will continue to" +

- " drop quickly enough that 2013 is a viable deadline.<BR><B>Source:</B>" +

- " X Prize Foundation. $10 Million Archon XPrize For Genomics." +

- " &quot;Archon&nbsp;<BR>Genomics X Prize: Competition Guidelines.&quot;" +

- " 2 Nov. 2006.&nbsp;<BR>9 Aug. 2008" +

- " &lt;<A HREF=\"http://genomics.xprize.org/files/downloads/genomics/archon_x_prize_for_genomics_competiton_guidelines.pdf\" TARGET=\"_blank\"><FONT COLOR=\"#0000ff\"><U>http://genomics.xprize.org/files/downloads/genomics/archon_x_prize_for_genomics_competiton_guidelines.pdf</U></FONT></A>&gt;.</P>" +

- "</UL>" +

- "<P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><BR>" +

- "</P>" +

- "<UL>" +

- " <LI><P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><B>Claim:</B> The" +

- " NHGRI, a government institute, has awarded grants to researchers" +

- " with the goal of reducing sequencing costs to $1,000/human" +

- " genome.<BR><B>Implication:</B> A price of $1,000 per genome is a" +

- " viable research goal in the near term and is already being actively" +

- " funded.<BR><B>Source: </B>National Institutes of Health. National" +

- " Human Genome Research&nbsp;<BR>Institute. &quot;NHGRI Aims to Make" +

- " DNA Sequencing Faster, More Cost&nbsp;<BR>Effective.&quot; Press" +

- " release. 4 Oct. 2006. 9 Aug. 2008 &lt;<A HREF=\"http://www.genome.gov/19518500\" TARGET=\"_blank\"><FONT COLOR=\"#0000ff\"><U>http://www.genome.gov/19518500</U></FONT></A>&gt;.</P>" +

- "</UL>" +

- "<P CLASS=\"western\" STYLE=\"margin-left: 0.25in; margin-bottom: 0in\"><BR>" +

- "</P>" +

- "<UL>" +

- " <LI><P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><B>Claim:</B> 23" +

- " and Me, a private company, will sequence 600,000 single nucleotide" +

- " polymorphisms (SNPs) for private individuals for" +

- " $1,000.<BR><B>Implication:</B> There is a large consumer demand for" +

- " genomics, so research in this area will continue to be heavily" +

- " funded.<BR><B>Source:</B> &quot;23 and Me.&quot; 24 July 2008. 9" +

- " Aug. 2008 &lt;<A HREF=\"https://www.23andme.com/\" TARGET=\"_blank\"><FONT COLOR=\"#0000ff\"><U>https://www.23andme.com/</U></FONT></A>&gt;.</P>" +

- "</UL>" +

- "<P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><BR>" +

- "</P>" +

- "<P CLASS=\"western\" STYLE=\"margin-bottom: 0in\"><A NAME=\"0.1_01000004\"></A><A NAME=\"0.1_0100000A\"></A>" +

- "<BR>" +

- "</P>" +

- "</BODY>" +

- "</html>"

- );

-

- UiText Q1_2_TEXT = new UiText("When will computing power stop improving exponentially?",

- "TODO: implement help text");

-

- UiText Q1_3_TEXT = new UiText("How much computing power for neuromorphic (brain-like) AI?",

- "TODO: implement help text");

-

- UiText Q1_4_TEXT = new UiText("How much funding will the largest neuromorphic AI projects have to buy computers with?",

- "TODO: implement help text");

-

- UiText Q1_5_TEXT = new UiText("When will we have enough brain imaging technology that we can build neuromorphic (brain-like) AI?",

- "TODO: implement help text");

-

- UiText C1_5_TEXT = new UiText("<html><span style='color: #000;'><strong>The future, according to you:</strong><br> " +

-"Based on your answers, here is the (preliminary) probability that neuromorphic artificial intelligence will have been " + "created at different points in the future:</span>",

- "TODO: implement help text");

-

- UiText Q1_6_TEXT = new UiText("What about non-neuromorphic AI? When will that be feasible?",

- "TODO: implement help text");

-

- UiText C1_6_TEXT = new UiText("<html><span style='color: #000;'><strong>The future, according to you:</strong><br> " +

-"Based on your answers, here is the (preliminary) probability that artificial intelligence of any sort " +

-"(neuromorphic or non-neuromorphic) will have been created at different points in the future:</span>",

- "TODO: implement help text");

-

- UiText Q1_7_TEXT = new UiText("What is the probability per year of major nuclear war that disrupts scientific progress?",

- "TODO: implement help text");

-

- UiText Q1_8_TEXT = new UiText("What is the probability per year of a non-nuclear catastrophe that disrupts scientific progress?",

- "TODO: implement help text");

-

- UiText C1_8_TEXT = new UiText("<html><span style='color: #000;'><strong>The future, according to you:<br></strong> " +

-"Based on your answers, here is the probability that a science-disrupting disaster will have occurred by " +

-"different points in the future (assuming, for now, that AI has not occurred):</span>",

- "TODO: implemente help text");

-

- UiText Q1_9_1_TEXT = new UiText("When will it be possible to sequence an entire human genome for less than $1000?",

- "TODO: implement help text");

-

- UiText Q1_9_2_TEXT = new UiText("How long before artificial gametes?",

- "TODO: implement help text");

-

- UiText Q1_9_3_TEXT = new UiText("How much do genetic variations contribute to scientific achievement?",

- "TODO: implement help text");

-

- UiText Q1_9_4_TEXT = new UiText("How widely will iterated embryo selection technology be adopted?",

- "TODO: implement help text");

-

- UiText Q1_9_5_TEXT = new UiText("What about other methods for enhancing human intelligence?",

- "TODO: implement help text");

-

- UiText C1_9_TEXT = new UiText("<html><span style='color:#000;'<strong>The future, according to you:</strong><br> " +

-"Based on your answers, here is the probability that artificial intelligence will have been created at " +

-"different points in the future. This time, we're factoring in your probabilities for science-disrupting " +

-"disaster and for intelligence enhancement that speeds AI research.</span>",

- "TODO: implement help text");

-

- UiText C1_10_TEXT = new UiText("<html><span style='color: #000;'><strong>The future, according to you:</strong><br> " +

-"Based on your answers, here is the probability that there will have been a civilization-scale disruption " +

-"to business as usual by different points in the future.</span>",

- "TODO: implement help text");

-}

-package org.singinst.uf.model;

-

-public interface ValueListener {

- void fireUpdate(double value);

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.HashMap;

-import java.util.List;

-import java.util.Map;

-

-import org.singinst.uf.presenter.HtmlUtil;

-import org.singinst.uf.presenter.LineBounds;

-import org.singinst.uf.presenter.SimpleStyle;

-

-public abstract class YearExtremeNodeMetadataContentsFactory {

-

- private final List<ScalarSchema> scalarSchemata = new ArrayList<ScalarSchema>();

- private final Map<NotablePercentile, ScalarSchema> scalarMap = new HashMap<NotablePercentile, ScalarSchema>();

- private final double year;

-

- protected YearExtremeNodeMetadataContentsFactory(Node node, LineBounds rangeBounds, double year,

- String subPrefix, String units, String adjSmall, String adjLarge) {

- this.year = year;

- for (NotablePercentile percentile : NotablePercentile.values()) {

- SimpleStyle style = new SimpleStyle(percentile.getColor());

-

- ScalarSchema scalarSchema = new ScalarSchema(

- node,

- ScalarSubIDString.yearPercentileID(year, percentile.getValue()),

- //"y" + year + "k" + percentile.getValue(),

- rangeBounds,

- units,

- HtmlUtil.style(style, percentile.getText(adjSmall, adjLarge) + subPrefix + ": "),

- //"y" + year + "k" + percentile.getValue(),

- "", null, true);

- ScalarValueHolder scalarValueHolder = scalarSchema.getScalarValueHolder();

- scalarValueHolder.setValue(scalarValueHolder.getValue() + percentile.getOffset() / 2);

- getScalars().add(scalarSchema);

- scalarMap.put(percentile, scalarSchema);

- }

- new NormalConstraint(scalarSchemata, -50).constrain();

- }

-

- public List<ScalarSchema> getScalars() {

- return scalarSchemata;

- }

-

- public ScalarSchema getScalarSchema(NotablePercentile percentile) {

- return scalarMap.get(percentile);

- }

-

- public double getYear() {

- return year;

- }

-

- public final String getYearString() {

- return "" + (int) year;

- }

-

- public abstract ConclusionReportGenerator getConclusionGenerator();

-}

-package org.singinst.uf.model;

-

-import java.util.ArrayList;

-import java.util.List;

-

-abstract public class YearwiseCalculationRelation extends CalculationRelation {

-

- public YearwiseCalculationRelation(Node node) {

- super(node);

- }

-

- @Override

- protected List<Calculation> getCalculations(List<Double> years) {

- List<Calculation> retVal = new ArrayList<Calculation>();

- for (double year : years) {

- retVal.add(getCalculation(year));

- }

- return retVal;

- }

-

- abstract protected Calculation getCalculation(double year);

-}

-package org.singinst.uf.presenter;

-

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.model.Axis;

-import org.singinst.uf.model.ScalarRelation;

-

-public abstract class AxisDirection {

-

- public static final AxisDirection X = new AxisDirection() {

-

- @Override

- protected void drawPerpindicularLine(GraphCanvas canvas,

- double parallelOffset, double perpindicularOffset1,

- double perpindicularOffset2) {

- canvas.drawLine(SimpleLine.vertical(parallelOffset, perpindicularOffset1, perpindicularOffset2));

- }

-

- @Override

- protected Axis getAxis(ScalarRelation relation) {

- return relation.getDomain();

- }

-

- @Override

- protected Axis getPerpindicularAxis(ScalarRelation relation) {

- return relation.getRange();

- }

-

- @Override

- protected void write(GraphCanvas hypothesisCanvas, ScalarRelation relation,

- double parallelOffset,

- double centParallel, double centPerpindicular,

- CanvasString canvasString) {

- hypothesisCanvas.write(

- new SimplePoint(parallelOffset - 1.5 * centParallel,

- relation.getRange().getLineBounds().getLowerBound() - 5 * centPerpindicular),

- canvasString);

- }

- };

-

- public static final AxisDirection Y = new AxisDirection() {

-

- @Override

- protected void drawPerpindicularLine(GraphCanvas canvas,

- double parallelOffset, double perpindicularOffset1,

- double perpindicularOffset2) {

- canvas.drawLine(SimpleLine.horizontal(perpindicularOffset1, perpindicularOffset2, parallelOffset));

- }

-

- @Override

- protected Axis getAxis(ScalarRelation relation) {

- return relation.getRange();

- }

-

- @Override

- protected Axis getPerpindicularAxis(ScalarRelation relation) {

- return relation.getDomain();

- }

-

- @Override

- protected void write(GraphCanvas hypothesisCanvas, ScalarRelation relation,

- double parallelOffset,

- double centParallel, double centPerpindicular,

- CanvasString canvasString) {

- hypothesisCanvas.write(

- new SimplePoint(relation.getDomain().getLineBounds().getLowerBound() - 5 * centPerpindicular,

- parallelOffset - 2 * centParallel),

- canvasString);

- }

- };

-

- public void draw(GraphCanvas hypothesisCanvas, ScalarRelation relation) {

- if (!getAxis(relation).isVisibleAxis()) {

- return;

- }

-

- LineBounds parallelBounds = getAxis(relation).getLineBounds();

- LineBounds perpindicularBounds = getPerpindicularAxis(relation).getLineBounds();

- double centParallel = parallelBounds.getLength() / 100;

- double centPerpindicular = perpindicularBounds.getLength() / 100;

-

- for (AxisSample axisSample : parallelBounds.getAxisSamples(false)) {

- double parallelOffset = axisSample.getValue();

- drawPerpindicularLine(hypothesisCanvas,

- parallelOffset,

- perpindicularBounds.getLowerBound() - centPerpindicular,

- perpindicularBounds.getLowerBound() + centPerpindicular);

- }

- hypothesisCanvas.pushStyle(SimpleStyle.PLAIN);

- for (AxisSample axisSample : parallelBounds.getAxisSamples(true)) {

- double parallelOffset = axisSample.getValue();

- drawPerpindicularLine(hypothesisCanvas,

- parallelOffset,

- perpindicularBounds.getLowerBound() - centPerpindicular,

- perpindicularBounds.getLowerBound() + centPerpindicular);

- write(hypothesisCanvas, relation, parallelOffset, centParallel, centPerpindicular, axisSample.getLabel());

- }

- hypothesisCanvas.popStyle();

- }

-

- protected abstract void write(GraphCanvas hypothesisCanvas, ScalarRelation relation,

- double parallelOffset,

- double centParallel, double centPerpindicular,

- CanvasString canvasString);

-

- protected abstract void drawPerpindicularLine(GraphCanvas canvas,

- double parallelOffset, double perpindicularOffset1, double perpindicularOffset2);

-

- protected abstract Axis getAxis(ScalarRelation relation);

- protected abstract Axis getPerpindicularAxis(ScalarRelation relation);

-

-}

-package org.singinst.uf.presenter;

-

-public class AxisSample {

-

- private final double value;

- private final CanvasString label;

-

- public AxisSample(double value) {

- this(value, null);

- }

-

- public AxisSample(double value, CanvasString label) {

- this.value = value;

- this.label = label;

- }

-

- public double getValue() {

- return value;

- }

-

- public CanvasString getLabel() {

- return label;

- }

-

-}

-package org.singinst.uf.presenter;

-

-public class CanvasString {

-

- private final String main;

- private final String optionalPower;

-

- public CanvasString(String main, String optionalPower) {

- this.main = main;

- this.optionalPower = optionalPower;

-

- }

-

- public CanvasString(String main) {

- this(main, null);

-

- }

-

- public String getMain() {

- return main;

- }

-

- public String getOptionalPower() {

- return optionalPower;

- }

-

-}

-package org.singinst.uf.presenter;

-

-import org.singinst.uf.math.SimplePoint;

-

-public class ClickableCurve extends ScalarValuePointList implements MouseClickListener {

- private final MouseClickListener listener;

-

- public ClickableCurve(MouseClickListener listener) {

- this.listener = listener;

-

- }

-

- public void draw(GraphCanvas canvas) {

- super.draw(canvas);

- canvas.addMouseClickListener(listener);

- }

-

- public void mouseClick(SimplePoint point) {

-

- }

-}

-package org.singinst.uf.presenter;

-

-

-public class ClippedCanvas extends ProxyCanvas {

-

- private final PlaneBounds clipBounds;

-

- public ClippedCanvas(GraphCanvas underlyingCanvas,

- PlaneBounds clipBounds) {

- super(underlyingCanvas);

- this.clipBounds = clipBounds;

- }

-

- public PlaneBounds getPlaneBounds() {

- return clipBounds;

- }

-

- GraphTransform proxyToUnderlyingCanvas() {

- return GraphTransform.IDENTITY;

- }

-

-}

-package org.singinst.uf.presenter;

-

-import java.util.ArrayList;

-import java.util.List;

-

-import org.singinst.uf.model.ValueListener;

-

-public class Completion {

- private static Completion singleton = new Completion();

- private String display;

- private int numStages;

- private int completed;

- private List<ValueListener> listeners = new ArrayList<ValueListener>();

-

- public static Completion getInstance() {

- return singleton;

- }

-

- public synchronized boolean init(String display, int numStages) {

- if (completed < this.numStages) {

- return false;

-

- }

- this.display = display;

- this.numStages = numStages;

- completed = 0;

- fireUpdate();

- return true;

- }

-

- private void fireUpdate() {

- for (ValueListener valueListener : listeners) {

- valueListener.fireUpdate(0);

- }

- }

-

- public synchronized void tick() {

- completed++;

- fireUpdate();

- }

-

- public synchronized int getCompleted() {

- return completed;

- }

-

- public synchronized int getNumStages() {

- return numStages;

- }

-

- public void addUpdateListener(ValueListener valueListener) {

- listeners.add(valueListener);

- }

-

- public String getDisplay() {

- return display;

- }

-

- public void destroy() {

- listeners.clear();

- }

-}

-package org.singinst.uf.presenter;

-

-import java.util.Arrays;

-import java.util.Comparator;

-import java.util.List;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.math.SimplePoint;

-

-public abstract class DraggableLine extends ScalarValuePointList implements MouseDragListener {

- private final int selectionPriority;

-

- public DraggableLine(int selectionPriority, SimpleStyle style) {

- super(style);

- this.selectionPriority = selectionPriority;

- }

-

- public void setLine(SimpleLine line) {

- setHypothesisPoints(Arrays.asList(line.p1, line.p2));

- }

-

- public int getSelectionPriority() {

- return selectionPriority;

- }

-

- @Override

- public void draw(GraphCanvas canvas) {

- super.draw(canvas);

- canvas.addMouseDragListener(this);

- }

-

- @Override

- public void setHypothesisPoints(List<SimplePoint> hypothesisPoints) {

- if (hypothesisPoints.size() != 2) {

- throw new IllegalArgumentException();

- }

- super.setHypothesisPoints(hypothesisPoints);

- }

-

- public abstract void dragTo(SimplePoint point);

-

- public int mouseDown(SimplePoint point) {

- double y = MathUtil.interpolate(getHypothesisPoints().get(0), getHypothesisPoints().get(1), point.x);

- if (Math.abs(y - point.y) < 0.5) {

- return selectionPriority;

- } else {

- return 0;

- }

- }

-

- public static final Comparator<DraggableLine> PRIORITY_COMPARATOR = new Comparator<DraggableLine>() {

-

- public int compare(DraggableLine o1, DraggableLine o2) {

- return ((Integer)o1.getSelectionPriority()).compareTo(o2.getSelectionPriority());

- }

-

- };

-}

-package org.singinst.uf.presenter;

-

-import java.util.List;

-

-import org.singinst.uf.math.SimplePoint;

-

-public interface GraphCanvas extends PlaneBounded {

-

- void clear();

-

- void drawLine(SimpleLine line);

-

- void drawDecorationPoint(SimplePoint point);

-

- void drawCurve(List<SimplePoint> points);

-

- void addMouseDragListener(MouseDragListener listener);

- void addMouseClickListener(MouseClickListener listener);

-

- void invalidate();

-

- void pushStyle(SimpleStyle style);

- void popStyle();

-

- void write(SimplePoint point, CanvasString canvasString);

-

-}

-package org.singinst.uf.presenter;

-

-import org.singinst.uf.math.InvertableFunction;

-import org.singinst.uf.math.LinearTransform;

-import org.singinst.uf.math.SimplePoint;

-

-public class GraphTransform {

-

- public static final GraphTransform IDENTITY = new GraphTransform(LinearTransform.IDENTITY, LinearTransform.IDENTITY);

- private final InvertableFunction transformX;

- private final InvertableFunction transformY;

-

- public GraphTransform(PlaneBounded from, PlaneBounded to) {

- transformX = new LinearTransform(from.getPlaneBounds().getBoundsX(), to.getPlaneBounds().getBoundsX());

- transformY = new LinearTransform(from.getPlaneBounds().getBoundsY(), to.getPlaneBounds().getBoundsY());

- }

-

- public GraphTransform(InvertableFunction transformX,

- InvertableFunction transformY) {

- this.transformX = transformX;

- this.transformY = transformY;

- }

-

- public SimplePoint apply(SimplePoint point) {

- return new SimplePoint(transformX.apply(point.x), transformY.apply(point.y));

- }

-

- public GraphTransform invert() {

- return new GraphTransform(transformX.invert(), transformY.invert());

- }

-

- public SimplePoint invert(SimplePoint point) {

- return invert().apply(point);

- }

-

-

-}

-package org.singinst.uf.presenter;

-

-import java.awt.Color;

-

-import org.singinst.uf.model.SimpleColor;

-

-public class HtmlUtil {

-

- public static String green(Object value) {

- return color("green", value);

- }

-

- /**

- * Takes a Color and an object, and wraps it around a span html element, and adds the color to it.

- *

- * @param c

- * Color to display value in

- * @param value

- * Value to display in color c

- * @return

- * HTML code.

- */

- public static String htmlcolorFromColor(Color c, Object value){

- return color("#" + Integer.toHexString((c.getRGB() & 0xffffff) | 0x1000000).substring(1) , value);

- }

-

- private static String color(String color, Object value) {

- return "<html><span color=" + color + ">" + value + "</span>";

- }

-

- public static String red(Object value) {

- return color("red", value);

- }

-

- public static String style(SimpleStyle style, String string) {

- return color(htmlColor(style), string);

- }

-

- private static String htmlColor(SimpleStyle style) {

- return new SimpleColor.Visitor<String>() {

-

- @Override

- public String visit_BLACK() {

- return "black";

- }

-

- @Override

- public String visit_GREEN() {

- return "green";

- }

-

- @Override

- public String visit_LIGHT_GRAY() {

- return "light gray";

- }

-

- @Override

- public String visit_RED() {

- return "red";

- }

-

- }.visit(style.getColor());

- }

-}

-package org.singinst.uf.presenter;

-

-public interface LineBounded {

- LineBounds getLineBounds();

-}

-package org.singinst.uf.presenter;

-

-import java.util.ArrayList;

-import java.util.Collection;

-import java.util.Collections;

-import java.util.List;

-import java.util.SortedSet;

-import java.util.TreeSet;

-

-import org.singinst.uf.math.InvertableFunction;

-import org.singinst.uf.math.MathUtil;

-

-public class LineBounds implements LineBounded {

- private final double first;

- private final double second;

- private final boolean displayAsLog;

-

- public LineBounds(double first, double second) {

- this(first, second, false);

- }

-

- public LineBounds(double first, double second, boolean displayAsLog) {

- if (first == second) {

- throw new IllegalArgumentException();

- }

- this.first = first;

- this.second = second;

- this.displayAsLog = displayAsLog;

- }

-

- public double getLength() {

- return getSecond() - getFirst();

- }

-

- public double getLowerBound() {

- return getFirst();

- }

-

- public double getUpperBound() {

- return getSecond();

- }

-

- public LineBounds getLineBounds() {

- return this;

- }

-

- public double getMidpoint() {

- return constrain((getLowerBound() + getUpperBound()) / 2);

- }

-

- public Iterable<Double> getSpanningSamples(int sampleCount) {

- List<Double> samples = new ArrayList<Double>();

- for (int i = 0; i < sampleCount; i++) {

- samples.add(getUpperBound() * i / (sampleCount - 1)

- + getLowerBound() * (sampleCount - i - 1) / (sampleCount - 1)

- );

- }

- return samples;

- }

-

- public Iterable<Double> getVisualSamples(int maxSamples, double start, Collection<Double> requiredPoints) {

- double sampleSizeLowerBound = getLength() / maxSamples;

- double sampleSize = Math.pow(10, Math.ceil(Math.log10(sampleSizeLowerBound)));

- SortedSet<Double> samples = new TreeSet<Double>();

- for (double i = start; i <= getUpperBound(); i += sampleSize) {

- samples.add(i);

- }

- for (double i = start - sampleSize; i >= getLowerBound(); i -= sampleSize) {

- samples.add(i);

- }

- samples.addAll(requiredPoints);

- return samples;

- }

-

- public Iterable<AxisSample> getAxisSamples(boolean majorTick) {

- int maxReadableSamples = majorTick ? 10 : 100;

- List<AxisSample> retVal = new ArrayList<AxisSample>();

- for (double value : getVisualSamples(maxReadableSamples, getLowerBound(), Collections.<Double>emptyList())) {

- if (majorTick) {

- retVal.add(new AxisSample(value, getCanvasString(value)));

- } else {

- retVal.add(new AxisSample(value));

- }

- }

- return retVal;

- }

-

- protected CanvasString getCanvasString(double x) {

- if (displayAsLog) {

- return new CanvasString("" + 10, NumericEntry.formatAsScalar(x));

- } else {

- return new CanvasString(NumericEntry.formatAsScalar(x));

- }

- }

-

- public double getFirst() {

- return first;

- }

-

- public double getSecond() {

- return second;

- }

-

- public boolean displayAsLog(){

- return displayAsLog;

- }

-

- public InvertableFunction toDisplay() {

- return InvertableFunction.IDENTITY;

- }

-

- public String userFormat(double value) {

- return NumericEntry.getScalarFormat().format(value);

- }

-

- public double constrain(double rounded) {

- return MathUtil.bound(rounded, getFirst(), getSecond());

- }

-}

-package org.singinst.uf.presenter;

-

-import org.singinst.uf.math.SimplePoint;

-

-public interface MouseClickListener {

- void mouseClick(SimplePoint point);

-

-}

-package org.singinst.uf.presenter;

-

-import org.singinst.uf.math.SimplePoint;

-

-public interface MouseDragListener {

- int mouseDown(SimplePoint point);

- void dragTo(SimplePoint point);

-}

-package org.singinst.uf.presenter;

-

-import java.text.DecimalFormat;

-import java.text.Format;

-

-import org.singinst.uf.math.MathUtil;

-import org.singinst.uf.model.ScalarSchema;

-

-public class NumericEntry {

- private static String SCALAR_FORMAT_STRING;

- static {

- SCALAR_FORMAT_STRING = "##0.";

- for (int i = 0; i < ScalarSchema.getMaxDecimalDigits(); i++) {

- SCALAR_FORMAT_STRING += "#";

- }

- }

- public static Format getScalarFormat() {

- return new DecimalFormat(SCALAR_FORMAT_STRING);

- }

- public static String formatAsScalar(double value) {

- return getScalarFormat().format(value);

- }

-

- private static int MAX_COLUMNS = 4 + ScalarSchema.getMaxDecimalDigits();

-

- public static int maxColumns() {

- return MAX_COLUMNS;

- }

- public static double round(double roundMe) {

- return MathUtil.round(roundMe, ScalarSchema.getMaxDecimalDigits());

- }

-}

-package org.singinst.uf.presenter;

-

-public interface PlaneBounded {

-

- PlaneBounds getPlaneBounds();

-}

-package org.singinst.uf.presenter;

-

-

-public class PlaneBounds implements PlaneBounded {

-

- private LineBounds boundsX;

- private LineBounds boundsY;

-

- public PlaneBounds(double left, double right, double bottom, double top) {

- boundsX = new LineBounds(left, right);

- boundsY = new LineBounds(bottom, top);

- }

-

- public PlaneBounds(LineBounded boundedX, LineBounded boundedY) {

- this.boundsX = boundedX.getLineBounds();

- this.boundsY = boundedY.getLineBounds();

- }

-

- public LineBounds getBoundsX() {

- return boundsX;

- }

-

- public LineBounds getBoundsY() {

- return boundsY;

- }

-

- public PlaneBounds getPlaneBounds() {

- return this;

- }

-

-}

-package org.singinst.uf.presenter;

-

-import java.util.ArrayList;

-import java.util.HashSet;

-import java.util.List;

-import java.util.Set;

-import java.util.SortedMap;

-import java.util.TreeMap;

-

-import org.singinst.uf.math.SimplePoint;

-

-abstract class ProxyCanvas implements GraphCanvas, MouseDragListener, MouseClickListener {

- private final GraphCanvas underlyingCanvas;

- private final Set<MouseDragListener> dragListeners = new HashSet<MouseDragListener>();

- private final Set<MouseClickListener> clickListeners = new HashSet<MouseClickListener>();

- private MouseDragListener selectedDragListener;

-

- ProxyCanvas(GraphCanvas underlyingCanvas) {

- this.underlyingCanvas = underlyingCanvas;

- }

-

- public void clear() {

- getUnderlyingCanvas().clear();

- }

-

- public void drawDecorationPoint(SimplePoint point) {

- getUnderlyingCanvas().drawDecorationPoint(toUnderlyingCanvas(point));

- }

-

- private SimplePoint toUnderlyingCanvas(SimplePoint point) {

- return proxyToUnderlyingCanvas().apply(point);

- }

-

- private SimplePoint fromUnderlyingCanvas(SimplePoint point) {

- return proxyToUnderlyingCanvas().invert(point);

- }

-

- abstract GraphTransform proxyToUnderlyingCanvas();

-

- public void drawLine(SimpleLine line) {

- getUnderlyingCanvas().drawLine(new SimpleLine(toUnderlyingCanvas(line.p1), toUnderlyingCanvas(line.p2)));

- }

-

- public void drawCurve(List<SimplePoint> points) {

- List<SimplePoint> underlyingCanvasPoints = new ArrayList<SimplePoint>();

- for (SimplePoint point : points) {

- underlyingCanvasPoints.add(toUnderlyingCanvas(point));

- }

- getUnderlyingCanvas().drawCurve(underlyingCanvasPoints);

- }

-

- public void addMouseDragListener(final MouseDragListener listener) {

- dragListeners.add(listener);

- getUnderlyingCanvas().addMouseDragListener(this);

- }

-

- public void addMouseClickListener(final MouseClickListener listener) {

- clickListeners.add(listener);

- getUnderlyingCanvas().addMouseClickListener(this);

- }

-

- public void mouseClick(SimplePoint point) {

- SimplePoint ourPoint = fromUnderlyingCanvas(point);

- for (MouseClickListener listener : clickListeners) {

- listener.mouseClick(ourPoint);

- }

- }

-

- public void dragTo(SimplePoint point) {

- SimplePoint ourPoint = fromUnderlyingCanvas(point);

- selectedDragListener.dragTo(ourPoint);

- }

-

- public int mouseDown(SimplePoint point) {

- SimplePoint ourPoint = fromUnderlyingCanvas(point);

-

- SortedMap<Integer, MouseDragListener> listenersByPriority = new TreeMap<Integer, MouseDragListener>();

- for (MouseDragListener listener : dragListeners) {

- listenersByPriority.put(listener.mouseDown(ourPoint), listener);

- }

- int maxPriority = listenersByPriority.lastKey();

- if (maxPriority > 0) {

- selectedDragListener = listenersByPriority.get(maxPriority);

- }

- return maxPriority;

- }

-

-

-

- public void invalidate() {

- underlyingCanvas.invalidate();

- }

-

-

-

- public void pushStyle(SimpleStyle style) {

- underlyingCanvas.pushStyle(style);

- }

-

-

-

- public void popStyle() {

- underlyingCanvas.popStyle();

- }

-

- public GraphCanvas getUnderlyingCanvas() {

- return underlyingCanvas;

- }

-

- public void write(SimplePoint point, CanvasString canvasString) {

- underlyingCanvas.write(toUnderlyingCanvas(point), canvasString);

- }

-

-

-}

-package org.singinst.uf.presenter;

-

-import java.util.List;

-

-import org.singinst.uf.math.SimplePoint;

-import org.singinst.uf.model.ScalarValueHolder;

-import org.singinst.uf.model.ScalarRelation;

-import org.singinst.uf.model.ValueListener;

-

-public class RelationPresentation implements ValueListener {

-

- private static final SimplePoint ORIGIN = new SimplePoint(0,0);

- private static final SimplePoint UPPER_LEFT = new SimplePoint(0,1);

- private static final SimplePoint LOWER_RIGHT = new SimplePoint(1,0);

- private final GraphCanvas viewCanvas;

- private final ScalarRelation relation;

-

- private final GraphCanvas unitPlusMarginCanvas;

- private final GraphCanvas hypothesisCanvas;

-

- public RelationPresentation(GraphCanvas viewCanvas, ScalarRelation relation) {

- this.viewCanvas = viewCanvas;

- this.relation = relation;

-

- unitPlusMarginCanvas = new ScaledCanvas(viewCanvas,

- new PlaneBounds(-0.1, 1.06, -0.1, 1.02));

- GraphCanvas unitCanvas = new ClippedCanvas(unitPlusMarginCanvas, new PlaneBounds(0, 1, 0, 1));

- hypothesisCanvas = new ScaledCanvas(unitCanvas, new PlaneBounds(relation.getDomain(), relation.getRange()));

-

- for (ScalarValueHolder scalarValueHolder : relation.getScalarValues()) {

- scalarValueHolder.addUpdateListener(this);

- }

- }

-

-

- public Runnable draw() {

- viewCanvas.clear();

-

- for (SimplePoint point : relation.decorationPoints) {

- hypothesisCanvas.pushStyle(SimpleStyle.DECORATION_POINT);

- hypothesisCanvas.drawDecorationPoint(point);

- hypothesisCanvas.popStyle();

- }

-

- drawAxes();

-

- List<? extends ScalarValuePointList> pointLists = relation.getPointLists();

- for (ScalarValuePointList pointList : pointLists) {

- if (pointList.getHypothesisPoints() == null) {

- return pointList.updater();

- }

- pointList.draw(hypothesisCanvas);

- }

- return null;

- }

-

-

- private void drawAxes() {

- unitPlusMarginCanvas.pushStyle(SimpleStyle.AXIS);

- if (relation.getDomain().isVisibleAxis()) {

- unitPlusMarginCanvas.drawLine(new SimpleLine(ORIGIN, LOWER_RIGHT));

- }

- if (relation.getRange().isVisibleAxis()) {

- unitPlusMarginCanvas.drawLine(new SimpleLine(ORIGIN, UPPER_LEFT));

- }

-

- AxisDirection.X.draw(hypothesisCanvas, relation);

- AxisDirection.Y.draw(hypothesisCanvas, relation);

-

- unitPlusMarginCanvas.popStyle();

- }

-

- public void fireUpdate(double value) {

- viewCanvas.invalidate();

- }

-}

-package org.singinst.uf.presenter;

-

-import java.util.List;

-

-import org.singinst.uf.math.SimplePoint;

-

-

-public class ScalarValuePointList {

-

- private List<SimplePoint> hypothesisPoints;

- private final SimpleStyle style;

-

- public ScalarValuePointList() {

- this(null);

- }

-

- public ScalarValuePointList(SimpleStyle style) {

- this.style = style;

- }

-

- public void setHypothesisPoints(List<SimplePoint> hypothesisPoints) {

- this.hypothesisPoints = hypothesisPoints;

- }

-

- public void draw(GraphCanvas canvas) {

- canvas.pushStyle(style);

- canvas.drawCurve(getHypothesisPoints());

- canvas.popStyle();

- }

-

- public List<SimplePoint> getHypothesisPoints() {

- return hypothesisPoints;

- }

-

- public Runnable updater() {