org.apache.mahout.math.jet.random.Gamma类的使用及代码示例

x33g5p2x  于2022-01-20 转载在 其他  
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本文整理了Java中org.apache.mahout.math.jet.random.Gamma类的一些代码示例，展示了Gamma类的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Gamma类的具体详情如下：
包路径：org.apache.mahout.math.jet.random.Gamma
类名称：Gamma

Gamma介绍

暂无

代码示例

代码示例来源：origin: tdunning/t-digest

@Override
  AbstractDistribution create(Random random) {
    return new Gamma(0.1, 0.1, random);
  }
},

代码示例来源：origin: apache/mahout

@Test
public void testNextDouble() {
 double[] z = new double[100000];
 Random gen = RandomUtils.getRandom();
 for (double alpha : new double[]{1, 2, 10, 0.1, 0.01, 100}) {
  Gamma g = new Gamma(alpha, 1, gen);
  for (int i = 0; i < z.length; i++) {
   z[i] = g.nextDouble();
  }
  Arrays.sort(z);
  // verify that empirical CDF matches theoretical one pretty closely
  for (double q : seq(0.01, 1, 0.01)) {
   double p = z[(int) (q * z.length)];
   assertEquals(q, g.cdf(p), 0.01);
  }
 }
}

代码示例来源：origin: apache/mahout

/**
 * Constructs a Gamma distribution with a given shape (alpha) and rate (beta).
 *
 * @param alpha The shape parameter.
 * @param rate The rate parameter.
 * @param randomGenerator The random number generator that generates bits for us.
 * @throws IllegalArgumentException if <tt>alpha &lt;= 0.0 || alpha &lt;= 0.0</tt>.
 */
public Gamma(double alpha, double rate, Random randomGenerator) {
 this.alpha = alpha;
 this.rate = rate;
 setRandomGenerator(randomGenerator);
}

代码示例来源：origin: apache/mahout

private static void checkGammaCdf(double alpha, double beta, double... values) {
 Gamma g = new Gamma(alpha, beta, RandomUtils.getRandom());
 int i = 0;
 for (double x : seq(0, 2 * alpha, 2 * alpha / 10)) {
  assertEquals(String.format(Locale.ENGLISH, "alpha=%.2f, i=%d, x=%.2f", alpha, i, x),
                   values[i], g.cdf(x), 1.0e-7);
  i++;
 }
}

代码示例来源：origin: apache/mahout

@Test
 public void testPdf() {
  Random gen = RandomUtils.getRandom();
  for (double alpha : new double[]{0.01, 0.1, 1, 2, 10, 100}) {
   for (double beta : new double[]{0.1, 1, 2, 100}) {
    Gamma g1 = new Gamma(alpha, beta, gen);
    for (double x : seq(0, 0.99, 0.1)) {
     double p = Math.pow(beta, alpha) * Math.pow(x, alpha - 1) *
      Math.exp(-beta * x - org.apache.mahout.math.jet.stat.Gamma.logGamma(alpha));
     assertEquals(String.format(Locale.ENGLISH, "alpha=%.2f, beta=%.2f, x=%.2f\n", alpha, beta, x),
      p, g1.pdf(x), 1.0e-9);
    }
   }
  }
 }
}

代码示例来源：origin: apache/mahout

@Test(timeout=50000)
public void testTimesSparseEfficiency() {
 Random raw = RandomUtils.getRandom();
 Gamma gen = new Gamma(0.1, 0.1, raw);
  int[] values = new int[1000];
  for (int k = 0; k < 1000; k++) {
   int j = (int) Math.min(1000, gen.nextDouble());
   values[j]++;
  int[] values = new int[1000];
  for (int k = 0; k < 1000; k++) {
   int j = (int) Math.min(1000, gen.nextDouble());
   values[j]++;

代码示例来源：origin: apache/mahout

/** Returns a random number from the distribution. */
@Override
public double nextDouble() {
 return nextDouble(alpha, rate);
}

代码示例来源：origin: apache/mahout

/** Returns the probability distribution function.
 * @param x Where to compute the density function.
 *
 * @return The value of the gamma density at x.
 */
@Override
public double pdf(double x) {
 if (x < 0) {
  throw new IllegalArgumentException();
 }
 if (x == 0) {
  if (alpha == 1.0) {
   return rate;
  } else if (alpha < 1) {
   return Double.POSITIVE_INFINITY;
  } else {
   return 0;
  }
 }
 if (alpha == 1.0) {
  return rate * Math.exp(-x * rate);
 }
 return rate * Math.exp((alpha - 1.0) * Math.log(x * rate) - x * rate - logGamma(alpha));
}

代码示例来源：origin: apache/mahout

b = 1.0 + 0.36788794412 * alpha;              // Step 1
while (true) {
 double p = b * randomDouble();
 if (p <= 1.0) {                       // Step 2. Case gds <= 1
  gds = Math.exp(Math.log(p) / alpha);
  if (Math.log(randomDouble()) <= -gds) {
   return gds / rate;
  if (Math.log(randomDouble()) <= (alpha - 1.0) * Math.log(gds)) {
   return gds / rate;
double v1;
do {
 v1 = 2.0 * randomDouble() - 1.0;
 double v2 = 2.0 * randomDouble() - 1.0;
 v12 = v1 * v1 + v2 * v2;
} while (v12 > 1.0);
double u = randomDouble();
if (d * u <= t * t * t) {
 return gds / rate;
 double e;
 do {
  e = -Math.log(randomDouble());
  u = randomDouble();
  u = u + u - 1.0;
  sign_u = u > 0 ? 1.0 : -1.0;

代码示例来源：origin: apache/mahout

@Test(timeout=50000)
public void testTimesDenseEfficiency() {
 Random raw = RandomUtils.getRandom();
 Gamma gen = new Gamma(0.1, 0.1, raw);
  int[] values = new int[1000];
  for (int k = 0; k < 1000; k++) {
   int j = (int) Math.min(1000, gen.nextDouble());
   values[j]++;

代码示例来源：origin: apache/mahout

Gamma g1 = new Gamma(1, beta, gen);
Gamma g2 = new Gamma(1, 1, gen);
for (double x : seq(0, 0.99, 0.1)) {
 assertEquals(String.format(Locale.ENGLISH, "Rate invariance: x = %.4f, alpha = 1, beta = %.1f", x, beta),
  1 - Math.exp(-x * beta), g1.cdf(x), 1.0e-9);
 assertEquals(String.format(Locale.ENGLISH, "Rate invariance: x = %.4f, alpha = 1, beta = %.1f", x, beta),
  g2.cdf(beta * x), g1.cdf(x), 1.0e-9);
Gamma g = new Gamma(alpha, 1, gen);
for (double beta : new double[]{0.1, 1, 2, 100}) {
 Gamma g1 = new Gamma(alpha, beta, gen);
 for (double x : seq(0, 0.9999, 0.001)) {
  assertEquals(
   String.format(Locale.ENGLISH, "Rate invariance: x = %.4f, alpha = %.2f, beta = %.1f", x, alpha, beta),
   g.cdf(x * beta), g1.cdf(x), 0);

代码示例来源：origin: apache/mahout

/**
 * Returns a sample from this distribution.  The value returned will
 * be the number of negative samples required before achieving r
 * positive samples.  Each successive sample is taken independently
 * from a Bernouli process with probability p of success.
 *
 * The algorithm used is taken from J.H. Ahrens, U. Dieter (1974):
 * Computer methods for sampling from gamma, beta, Poisson and
 * binomial distributions, Computing 12, 223--246.
 *
 * This algorithm is essentially the same as described at
 * http://en.wikipedia.org/wiki/Negative_binomial_distribution#Gamma.E2.80.93Poisson_mixture
 * except that the notion of positive and negative outcomes is uniformly
 * inverted.  Because the inversion is complete and consistent, this
 * definition is effectively identical to that defined on wikipedia.
 */
public int nextInt(int r, double p) {
 return this.poisson.nextInt(gamma.nextDouble(r, p / (1.0 - p)));
}

代码示例来源：origin: cloudera/mahout

@Test
 public void testPdf() {
  Random gen = RandomUtils.getRandom();
  for (double alpha : new double[]{0.01, 0.1, 1, 2, 10, 100}) {
   for (double beta : new double[]{0.1, 1, 2, 100}) {
    Gamma g1 = new Gamma(alpha, beta, gen);
    for (double x : seq(0, 0.99, 0.1)) {
     double p = Math.pow(beta, alpha) * Math.pow(x, alpha - 1) *
      Math.exp(-beta * x - org.apache.mahout.math.jet.stat.Gamma.logGamma(alpha));
     assertEquals(String.format(Locale.ENGLISH, "alpha=%.2f, beta=%.2f, x=%.2f\n", alpha, beta, x),
      p, g1.pdf(x), 1.0e-9);
    }
   }
  }
 }
}

代码示例来源：origin: org.apache.mahout/mahout-math

/** Returns the probability distribution function.
 * @param x Where to compute the density function.
 *
 * @return The value of the gamma density at x.
 */
@Override
public double pdf(double x) {
 if (x < 0) {
  throw new IllegalArgumentException();
 }
 if (x == 0) {
  if (alpha == 1.0) {
   return rate;
  } else if (alpha < 1) {
   return Double.POSITIVE_INFINITY;
  } else {
   return 0;
  }
 }
 if (alpha == 1.0) {
  return rate * Math.exp(-x * rate);
 }
 return rate * Math.exp((alpha - 1.0) * Math.log(x * rate) - x * rate - logGamma(alpha));
}

代码示例来源：origin: org.apache.mahout/mahout-math

b = 1.0 + 0.36788794412 * alpha;              // Step 1
while (true) {
 double p = b * randomDouble();
 if (p <= 1.0) {                       // Step 2. Case gds <= 1
  gds = Math.exp(Math.log(p) / alpha);
  if (Math.log(randomDouble()) <= -gds) {
   return gds / rate;
  if (Math.log(randomDouble()) <= (alpha - 1.0) * Math.log(gds)) {
   return gds / rate;
double v1;
do {
 v1 = 2.0 * randomDouble() - 1.0;
 double v2 = 2.0 * randomDouble() - 1.0;
 v12 = v1 * v1 + v2 * v2;
} while (v12 > 1.0);
double u = randomDouble();
if (d * u <= t * t * t) {
 return gds / rate;
 double e;
 do {
  e = -Math.log(randomDouble());
  u = randomDouble();
  u = u + u - 1.0;
  sign_u = u > 0 ? 1.0 : -1.0;

代码示例来源：origin: apache/mahout

/**
 * Constructs a Negative Binomial distribution which describes the probability of getting
 * a particular number of negative trials (k) before getting a fixed number of positive
 * trials (r) where each positive trial has probability (p) of being successful.
 *
 * @param r               the required number of positive trials.
 * @param p               the probability of success.
 * @param randomGenerator a uniform random number generator.
 */
public NegativeBinomial(int r, double p, Random randomGenerator) {
 setRandomGenerator(randomGenerator);
 this.r = r;
 this.p = p;
 this.gamma = new Gamma(r, 1, randomGenerator);
 this.poisson = new Poisson(0.0, randomGenerator);
}

代码示例来源：origin: apache/mahout

@Test(timeout=50000)
public void testTimesOtherSparseEfficiency() {
 Random raw = RandomUtils.getRandom();
 Gamma gen = new Gamma(0.1, 0.1, raw);
 // build a sequential sparse matrix and a diagonal matrix and multiply them
 Matrix x = new SparseRowMatrix(1000, 2000, false);
 for (int i = 0; i < 1000; i++) {
  int[] values = new int[1000];
  for (int k = 0; k < 1000; k++) {
   int j = (int) Math.min(1000, gen.nextDouble());
   values[j]++;
  }
  for (int j = 0; j < 1000; j++) {
   if (values[j] > 0) {
    x.set(i, j, values[j]);
   }
  }
 }
 Vector d = new DenseVector(2000).assign(Functions.random());
 Matrix y = new DiagonalMatrix(d);
 long t0 = System.nanoTime();
 Matrix z = x.times(y);
 double elapsedTime = (System.nanoTime() - t0) * 1e-6;
 System.out.printf("done in %.1f ms\n", elapsedTime);
 for (MatrixSlice row : z) {
  for (Vector.Element element : row.nonZeroes()) {
   assertEquals(x.get(row.index(), element.index()) * d.get(element.index()), element.get(), 1e-12);
  }
 }
}

代码示例来源：origin: cloudera/mahout

@Test
public void testNextDouble() {
 double[] z = new double[100000];
 Random gen = RandomUtils.getRandom();
 for (double alpha : new double[]{1, 2, 10, 0.1, 0.01, 100}) {
  Gamma g = new Gamma(alpha, 1, gen);
  for (int i = 0; i < z.length; i++) {
   z[i] = g.nextDouble();
  }
  Arrays.sort(z);
  // verify that empirical CDF matches theoretical one pretty closely
  for (double q : seq(0.01, 1, 0.01)) {
   double p = z[(int) (q * z.length)];
   assertEquals(q, g.cdf(p), 0.01);
  }
 }
}

代码示例来源：origin: cloudera/mahout

private static void checkGammaCdf(double alpha, double beta, double... values) {
 Gamma g = new Gamma(alpha, beta, RandomUtils.getRandom());
 int i = 0;
 for (double x : seq(0, 2 * alpha, 2 * alpha / 10)) {
  assertEquals(String.format(Locale.ENGLISH, "alpha=%.2f, i=%d, x=%.2f", alpha, i, x),
                   values[i], g.cdf(x), 1.0e-7);
  i++;
 }
}

代码示例来源：origin: org.apache.mahout/mahout-math

/** Returns a random number from the distribution. */
@Override
public double nextDouble() {
 return nextDouble(alpha, rate);
}

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