// Copyright (c) 2019 by the SciSharp Team

// Code generated by CodeMinion: https://github.com/SciSharp/CodeMinion

using System;

using System.Collections;

using System.Collections.Generic;

using System.IO;

using System.Linq;

using System.Runtime.InteropServices;

using System.Text;

using Numpy.Models;

using Microsoft.VisualStudio.TestTools.UnitTesting;

using Python.Runtime;

using Assert = NUnit.Framework.Assert;

namespace Numpy.UnitTest

{

[TestClass]

public class NumPy_randomTest : BaseTestCase

{

[TestMethod]

public void randTest()

{

// >>> np.random.rand(3,2)

// array([[ 0.14022471, 0.96360618], #random

// [ 0.37601032, 0.25528411], #random

// [ 0.49313049, 0.94909878]]) #random

//

np.random.seed(0);

var given= np.random.rand(1,2);

var expected = "array([[0.5488135 , 0.71518937]])";

Assert.AreEqual(expected, given.repr);

np.random.seed(0);

float x = np.random.rand();

Assert.AreEqual(0.5488135039273248f, x);

}

[TestMethod]

public void randnTest()

{

// >>> np.random.randn()

// 2.1923875335537315 #random

//

np.random.seed(0);

var given = np.random.randn();

Assert.AreEqual(1.76405239f, given);

// Two-by-four array of samples from N(3, 6.25):

// >>> 2.5 * np.random.randn(2, 4) + 3

// array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random

// [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random

//

np.random.seed(0);

var a = 2.5 * np.random.randn(1, 2) + 3;

var expected= "array([[7.41013086, 4.00039302]])";

Assert.AreEqual(expected, a.repr);

}

[TestMethod]

public void randintTest()

{

// >>> np.random.randint(2, size=10)

// array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])

// >>> np.random.randint(1, size=10)

// array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

//

var given = np.random.randint(2, size: new int[] { 10 });

Assert.LessOrEqual( (int)np.sum(given), 10);

given = np.random.randint(1, size: new int[] { 10 });

var expected =

"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])";

Assert.AreEqual(expected, given.repr);

// Generate a 2 x 4 array of ints between 0 and 4, inclusive:

// >>> np.random.randint(5, size=(2, 4))

// array([[4, 0, 2, 1],

// [3, 2, 2, 0]])

//

given= np.random.randint(5, size:new int[]{2, 4});

Assert.LessOrEqual((int)given.max(), 4);

Assert.GreaterOrEqual((int)given.min(), 0);

}

[TestMethod]

public void random_integersTest()

{

// >>> np.random.random_integers(5)

// 4

// >>> type(np.random.random_integers(5))

// <type 'int'>

// >>> np.random.random_integers(5, size=(3,2))

// array([[5, 4],

// [3, 3],

// [4, 5]])

//

#if TODO

var given= np.random.random_integers(5);

var expected=

"4";

Assert.AreEqual(expected, given.repr);

given= type(np.random.random_integers(5));

expected=

"<type 'int'>";

Assert.AreEqual(expected, given.repr);

given= np.random.random_integers(5, size=(3,2));

expected=

"array([[5, 4],\n" +

" [3, 3],\n" +

" [4, 5]])";

Assert.AreEqual(expected, given.repr);

#endif

// Choose five random numbers from the set of five evenly-spaced

// numbers between 0 and 2.5, inclusive (i.e., from the set

// ):

// >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.

// array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ])

//

#if TODO

given= 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.;

expected=

"array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ])";

Assert.AreEqual(expected, given.repr);

#endif

// Roll two six sided dice 1000 times and sum the results:

// >>> d1 = np.random.random_integers(1, 6, 1000)

// >>> d2 = np.random.random_integers(1, 6, 1000)

// >>> dsums = d1 + d2

//

#if TODO

given= d1 = np.random.random_integers(1, 6, 1000);

given= d2 = np.random.random_integers(1, 6, 1000);

given= dsums = d1 + d2;

#endif

// Display results as a histogram:

// >>> import matplotlib.pyplot as plt

// >>> count, bins, ignored = plt.hist(dsums, 11, density=True)

// >>> plt.show()

//

#if TODO

given= import matplotlib.pyplot as plt;

given= count, bins, ignored = plt.hist(dsums, 11, density=True);

given= plt.show();

#endif

}

[TestMethod]

public void random_sampleTest()

{

// >>> np.random.random_sample()

// 0.47108547995356098

// >>> type(np.random.random_sample())

// <type 'float'>

// >>> np.random.random_sample((5,))

// array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])

//

#if TODO

var given= np.random.random_sample();

var expected=

"0.47108547995356098";

Assert.AreEqual(expected, given.repr);

given= type(np.random.random_sample());

expected=

"<type 'float'>";

Assert.AreEqual(expected, given.repr);

given= np.random.random_sample((5,));

expected=

"array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])";

Assert.AreEqual(expected, given.repr);

#endif

// Three-by-two array of random numbers from [-5, 0):

// >>> 5 * np.random.random_sample((3, 2)) - 5

// array([[-3.99149989, -0.52338984],

// [-2.99091858, -0.79479508],

// [-1.23204345, -1.75224494]])

//

#if TODO

given= 5 * np.random.random_sample((3, 2)) - 5;

expected=

"array([[-3.99149989, -0.52338984],\n" +

" [-2.99091858, -0.79479508],\n" +

" [-1.23204345, -1.75224494]])";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void randomTest()

{

// >>> np.random.random_sample()

// 0.47108547995356098

// >>> type(np.random.random_sample())

// <type 'float'>

// >>> np.random.random_sample((5,))

// array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])

//

#if TODO

var given= np.random.random_sample();

var expected=

"0.47108547995356098";

Assert.AreEqual(expected, given.repr);

given= type(np.random.random_sample());

expected=

"<type 'float'>";

Assert.AreEqual(expected, given.repr);

given= np.random.random_sample((5,));

expected=

"array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])";

Assert.AreEqual(expected, given.repr);

#endif

// Three-by-two array of random numbers from [-5, 0):

// >>> 5 * np.random.random_sample((3, 2)) - 5

// array([[-3.99149989, -0.52338984],

// [-2.99091858, -0.79479508],

// [-1.23204345, -1.75224494]])

//

#if TODO

given= 5 * np.random.random_sample((3, 2)) - 5;

expected=

"array([[-3.99149989, -0.52338984],\n" +

" [-2.99091858, -0.79479508],\n" +

" [-1.23204345, -1.75224494]])";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void ranfTest()

{

// >>> np.random.random_sample()

// 0.47108547995356098

// >>> type(np.random.random_sample())

// <type 'float'>

// >>> np.random.random_sample((5,))

// array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])

//

#if TODO

var given= np.random.random_sample();

var expected=

"0.47108547995356098";

Assert.AreEqual(expected, given.repr);

given= type(np.random.random_sample());

expected=

"<type 'float'>";

Assert.AreEqual(expected, given.repr);

given= np.random.random_sample((5,));

expected=

"array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])";

Assert.AreEqual(expected, given.repr);

#endif

// Three-by-two array of random numbers from [-5, 0):

// >>> 5 * np.random.random_sample((3, 2)) - 5

// array([[-3.99149989, -0.52338984],

// [-2.99091858, -0.79479508],

// [-1.23204345, -1.75224494]])

//

#if TODO

given= 5 * np.random.random_sample((3, 2)) - 5;

expected=

"array([[-3.99149989, -0.52338984],\n" +

" [-2.99091858, -0.79479508],\n" +

" [-1.23204345, -1.75224494]])";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void sampleTest()

{

// >>> np.random.random_sample()

// 0.47108547995356098

// >>> type(np.random.random_sample())

// <type 'float'>

// >>> np.random.random_sample((5,))

// array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])

//

#if TODO

var given= np.random.random_sample();

var expected=

"0.47108547995356098";

Assert.AreEqual(expected, given.repr);

given= type(np.random.random_sample());

expected=

"<type 'float'>";

Assert.AreEqual(expected, given.repr);

given= np.random.random_sample((5,));

expected=

"array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])";

Assert.AreEqual(expected, given.repr);

#endif

// Three-by-two array of random numbers from [-5, 0):

// >>> 5 * np.random.random_sample((3, 2)) - 5

// array([[-3.99149989, -0.52338984],

// [-2.99091858, -0.79479508],

// [-1.23204345, -1.75224494]])

//

#if TODO

given= 5 * np.random.random_sample((3, 2)) - 5;

expected=

"array([[-3.99149989, -0.52338984],\n" +

" [-2.99091858, -0.79479508],\n" +

" [-1.23204345, -1.75224494]])";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void choiceTest()

{

// Generate a uniform random sample from np.arange(5) of size 3:

// >>> np.random.choice(5, 3)

// array([0, 3, 4])

// >>> #This is equivalent to np.random.randint(0,5,3)

//

#if TODO

var given= np.random.choice(5, 3);

var expected=

"array([0, 3, 4])";

Assert.AreEqual(expected, given.repr);

given= #This is equivalent to np.random.randint(0,5,3);

#endif

// Generate a non-uniform random sample from np.arange(5) of size 3:

// >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])

// array([3, 3, 0])

//

#if TODO

given= np.random.choice(5, 3, p={0.1, 0, 0.3, 0.6, 0});

expected=

"array([3, 3, 0])";

Assert.AreEqual(expected, given.repr);

#endif

// Generate a uniform random sample from np.arange(5) of size 3 without

// replacement:

// >>> np.random.choice(5, 3, replace=False)

// array([3,1,0])

// >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]

//

#if TODO

given= np.random.choice(5, 3, replace=False);

expected=

"array([3,1,0])";

Assert.AreEqual(expected, given.repr);

given= #This is equivalent to np.random.permutation(np.arange(5)){:3};

#endif

// Generate a non-uniform random sample from np.arange(5) of size

// 3 without replacement:

// >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])

// array([2, 3, 0])

//

#if TODO

given= np.random.choice(5, 3, replace=False, p={0.1, 0, 0.3, 0.6, 0});

expected=

"array([2, 3, 0])";

Assert.AreEqual(expected, given.repr);

#endif

// Any of the above can be repeated with an arbitrary array-like

// instead of just integers. For instance:

// >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']

// >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])

// array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],

// dtype='|S11')

//

#if TODO

given= aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher'];

given= np.random.choice(aa_milne_arr, 5, p={0.5, 0.1, 0.1, 0.3});

expected=

"array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],\n" +

" dtype='|S11')";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void bytesTest()

{

// >>> np.random.bytes(10)

// ' eh\x85\x022SZ\xbf\xa4' #random

//

#if TODO

var given= np.random.bytes(10);

var expected=

"' eh\x85\x022SZ\xbf\xa4' #random";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void shuffleTest()

{

// >>> arr = np.arange(10)

// >>> np.random.shuffle(arr)

// >>> arr

// [1 7 5 2 9 4 3 6 0 8]

//

#if TODO

var given= arr = np.arange(10);

given= np.random.shuffle(arr);

given= arr;

var expected=

"[1 7 5 2 9 4 3 6 0 8]";

Assert.AreEqual(expected, given.repr);

#endif

// Multi-dimensional arrays are only shuffled along the first axis:

// >>> arr = np.arange(9).reshape((3, 3))

// >>> np.random.shuffle(arr)

// >>> arr

// array([[3, 4, 5],

// [6, 7, 8],

// [0, 1, 2]])

//

#if TODO

given= arr = np.arange(9).reshape((3, 3));

given= np.random.shuffle(arr);

given= arr;

expected=

"array([[3, 4, 5],\n" +

" [6, 7, 8],\n" +

" [0, 1, 2]])";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void permutationTest()

{

// >>> np.random.permutation(10)

// array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])

//

#if TODO

var given= np.random.permutation(10);

var expected=

"array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])";

Assert.AreEqual(expected, given.repr);

#endif

// >>> np.random.permutation([1, 4, 9, 12, 15])

// array([15, 1, 9, 4, 12])

//

#if TODO

given= np.random.permutation({1, 4, 9, 12, 15});

expected=

"array([15, 1, 9, 4, 12])";

Assert.AreEqual(expected, given.repr);

#endif

// >>> arr = np.arange(9).reshape((3, 3))

// >>> np.random.permutation(arr)

// array([[6, 7, 8],

// [0, 1, 2],

// [3, 4, 5]])

//

#if TODO

given= arr = np.arange(9).reshape((3, 3));

given= np.random.permutation(arr);

expected=

"array([[6, 7, 8],\n" +

" [0, 1, 2],\n" +

" [3, 4, 5]])";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void binomialTest()

{

// Draw samples from the distribution:

// >>> n, p = 10, .5 # number of trials, probability of each trial

// >>> s = np.random.binomial(n, p, 1000)

// # result of flipping a coin 10 times, tested 1000 times.

//

#if TODO

var given= n, p = 10, .5 # number of trials, probability of each trial;

given= s = np.random.binomial(n, p, 1000);

// result of flipping a coin 10 times, tested 1000 times.

#endif

// A real world example. A company drills 9 wild-cat oil exploration

// wells, each with an estimated probability of success of 0.1. All nine

// wells fail. What is the probability of that happening?

// Let’s do 20,000 trials of the model, and count the number that

// generate zero positive results.

// >>> sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.

// # answer = 0.38885, or 38%.

//

#if TODO

given= sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.;

// answer = 0.38885, or 38%.

#endif

}

[TestMethod]

public void chisquareTest()

{

// >>> np.random.chisquare(2,4)

// array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])

//

#if TODO

var given= np.random.chisquare(2,4);

var expected=

"array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void dirichletTest()

{

// Taking an example cited in Wikipedia, this distribution can be used if

// one wanted to cut strings (each of initial length 1.0) into K pieces

// with different lengths, where each piece had, on average, a designated

// average length, but allowing some variation in the relative sizes of

// the pieces.

// >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()

//

#if TODO

var given= s = np.random.dirichlet((10, 5, 3), 20).transpose();

#endif

// >>> import matplotlib.pyplot as plt

// >>> plt.barh(range(20), s[0])

// >>> plt.barh(range(20), s[1], left=s[0], color='g')

// >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')

// >>> plt.title("Lengths of Strings")

//

#if TODO

given= import matplotlib.pyplot as plt;

given= plt.barh(range(20), s[0]);

given= plt.barh(range(20), s[1], left=s[0], color='g');

given= plt.barh(range(20), s[2], left=s[0]+s[1], color='r');

given= plt.title("Lengths of Strings");

#endif

}

[TestMethod]

public void fTest()

{

// An example from Glantz[1], pp 47-40:

// Two groups, children of diabetics (25 people) and children from people

// without diabetes (25 controls). Fasting blood glucose was measured,

// case group had a mean value of 86.1, controls had a mean value of

// 82.2. Standard deviations were 2.09 and 2.49 respectively. Are these

// data consistent with the null hypothesis that the parents diabetic

// status does not affect their children’s blood glucose levels?

// Calculating the F statistic from the data gives a value of 36.01.

// Draw samples from the distribution:

// >>> dfnum = 1. # between group degrees of freedom

// >>> dfden = 48. # within groups degrees of freedom

// >>> s = np.random.f(dfnum, dfden, 1000)

//

#if TODO

var given= dfnum = 1. # between group degrees of freedom;

given= dfden = 48. # within groups degrees of freedom;

given= s = np.random.f(dfnum, dfden, 1000);

#endif

// The lower bound for the top 1% of the samples is :

// >>> sort(s)[-10]

// 7.61988120985

//

#if TODO

given= sort(s)[-10];

var expected=

"7.61988120985";

Assert.AreEqual(expected, given.repr);

#endif

// So there is about a 1% chance that the F statistic will exceed 7.62,

// the measured value is 36, so the null hypothesis is rejected at the 1%

// level.

}

[TestMethod]

public void gammaTest()

{

// Draw samples from the distribution:

// >>> shape, scale = 2., 2. # mean=4, std=2*sqrt(2)

// >>> s = np.random.gamma(shape, scale, 1000)

//

#if TODO

var given= shape, scale = 2., 2. # mean=4, std=2*sqrt(2);

given= s = np.random.gamma(shape, scale, 1000);

#endif

// Display the histogram of the samples, along with

// the probability density function:

// >>> import matplotlib.pyplot as plt

// >>> import scipy.special as sps

// >>> count, bins, ignored = plt.hist(s, 50, density=True)

// >>> y = bins**(shape-1)*(np.exp(-bins/scale) /

// ... (sps.gamma(shape)*scale**shape))

// >>> plt.plot(bins, y, linewidth=2, color='r')

// >>> plt.show()

//

#if TODO

given= import matplotlib.pyplot as plt;

given= import scipy.special as sps;

given= count, bins, ignored = plt.hist(s, 50, density=True);

given= y = bins**(shape-1)*(np.exp(-bins/scale) /;

var expected=

"... (sps.gamma(shape)*scale**shape))";

Assert.AreEqual(expected, given.repr);

given= plt.plot(bins, y, linewidth=2, color='r');

given= plt.show();

#endif

}

[TestMethod]

public void geometricTest()

{

// Draw ten thousand values from the geometric distribution,

// with the probability of an individual success equal to 0.35:

// >>> z = np.random.geometric(p=0.35, size=10000)

//

#if TODO

var given= z = np.random.geometric(p=0.35, size=10000);

#endif

// How many trials succeeded after a single run?

// >>> (z == 1).sum() / 10000.

// 0.34889999999999999 #random

//

#if TODO

given= (z == 1).sum() / 10000.;

var expected=

"0.34889999999999999 #random";

Assert.AreEqual(expected, given.repr);

#endif

}

[TestMethod]

public void gumbelTest()

{

// Draw samples from the distribution:

// >>> mu, beta = 0, 0.1 # location and scale

// >>> s = np.random.gumbel(mu, beta, 1000)

//

#if TODO

var given= mu, beta = 0, 0.1 # location and scale;

given= s = np.random.gumbel(mu, beta, 1000);

#endif

// Display the histogram of the samples, along with

// the probability density function:

// >>> import matplotlib.pyplot as plt

// >>> count, bins, ignored = plt.hist(s, 30, density=True)

// >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)

// ... * np.exp( -np.exp( -(bins - mu) /beta) ),

// ... linewidth=2, color='r')

// >>> plt.show()

//

#if TODO

given= import matplotlib.pyplot as plt;

given= count, bins, ignored = plt.hist(s, 30, density=True);

given= plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta);

var expected=

"... * np.exp( -np.exp( -(bins - mu) /beta) ),\n" +

"... linewidth=2, color='r')";

Assert.AreEqual(expected, given.repr);

given= plt.show();

#endif

// Show how an extreme value distribution can arise from a Gaussian process

// and compare to a Gaussian:

// >>> means = []

// >>> maxima = []

// >>> for i in range(0,1000) :

// ... a = np.random.normal(mu, beta, 1000)

// ... means.append(a.mean())

// ... maxima.append(a.max())

// >>> count, bins, ignored = plt.hist(maxima, 30, density=True)

// >>> beta = np.std(maxima) * np.sqrt(6) / np.pi

// >>> mu = np.mean(maxima) - 0.57721*beta

// >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)

// ... * np.exp(-np.exp(-(bins - mu)/beta)),

// ... linewidth=2, color='r')

// >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))

// ... * np.exp(-(bins - mu)**2 / (2 * beta**2)),

// ... linewidth=2, color='g')

// >>> plt.show()

//

#if TODO

given= means = [];

given= maxima = [];

given= for i in range(0,1000) :;

expected=

"... a = np.random.normal(mu, beta, 1000)\n" +

"... means.append(a.mean())\n" +

"... maxima.append(a.max())";

Assert.AreEqual(expected, given.repr);

given= count, bins, ignored = plt.hist(maxima, 30, density=True);

given= beta = np.std(maxima) * np.sqrt(6) / np.pi;

given= mu = np.mean(maxima) - 0.57721*beta;

given= plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta);

expected=

"... * np.exp(-np.exp(-(bins - mu)/beta)),\n" +

"... linewidth=2, color='r')";

Assert.AreEqual(expected, given.repr);

given= plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi));

expected=

"... * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n" +

"... linewidth=2, color='g')";

Assert.AreEqual(expected, given.repr);

given= plt.show();

#endif

}

[TestMethod]

public void hypergeometricTest()

{

// Draw samples from the distribution:

// >>> ngood, nbad, nsamp = 100, 2, 10

// # number of good, number of bad, and number of samples

// >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)

// >>> from matplotlib.pyplot import hist

// >>> hist(s)

// # note that it is very unlikely to grab both bad items

//

#if TODO

var given= ngood, nbad, nsamp = 100, 2, 10;

// number of good, number of bad, and number of samples

given= s = np.random.hypergeometric(ngood, nbad, nsamp, 1000);

given= from matplotlib.pyplot import hist;

given= hist(s);

// note that it is very unlikely to grab both bad items

#endif

// Suppose you have an urn with 15 white and 15 black marbles.

// If you pull 15 marbles at random, how likely is it that

// 12 or more of them are one color?

// >>> s = np.random.hypergeometric(15, 15, 15, 100000)

// >>> sum(s>=12)/100000. + sum(s<=3)/100000.

// # answer = 0.003 ... pretty unlikely!

//

#if TODO

given= s = np.random.hypergeometric(15, 15, 15, 100000);

given= sum(s>=12)/100000. + sum(s<=3)/100000.;

// answer = 0.003 ... pretty unlikely!

#endif

}

[TestMethod]

public void laplaceTest()

{

// Draw samples from the distribution

// >>> loc, scale = 0., 1.

// >>> s = np.random.laplace(loc, scale, 1000)

//

#if TODO

var given= loc, scale = 0., 1.;

given= s = np.random.laplace(loc, scale, 1000);

#endif

// Display the histogram of the samples, along with

// the probability density function:

// >>> import matplotlib.pyplot as plt

// >>> count, bins, ignored = plt.hist(s, 30, density=True)

// >>> x = np.arange(-8., 8., .01)

// >>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)

// >>> plt.plot(x, pdf)

//

#if TODO

given= import matplotlib.pyplot as plt;

given= count, bins, ignored = plt.hist(s, 30, density=True);

given= x = np.arange(-8., 8., .01);

given= pdf = np.exp(-abs(x-loc)/scale)/(2.*scale);

given= plt.plot(x, pdf);

#endif

// Plot Gaussian for comparison:

// >>> g = (1/(scale * np.sqrt(2 * np.pi)) *

// ... np.exp(-(x - loc)**2 / (2 * scale**2)))

// >>> plt.plot(x,g)

//

#if TODO

given= g = (1/(scale * np.sqrt(2 * np.pi)) *;

var expected=

"... np.exp(-(x - loc)**2 / (2 * scale**2)))";

Assert.AreEqual(expected, given.repr);

given= plt.plot(x,g);

#endif

}

[TestMethod]

public void logisticTest()

{

// Draw samples from the distribution:

// >>> loc, scale = 10, 1

// >>> s = np.random.logistic(loc, scale, 10000)

// >>> import matplotlib.pyplot as plt

// >>> count, bins, ignored = plt.hist(s, bins=50)

//

#if TODO

var given= loc, scale = 10, 1;

given= s = np.random.logistic(loc, scale, 10000);

given= import matplotlib.pyplot as plt;

given= count, bins, ignored = plt.hist(s, bins=50);

#endif

// # plot against distribution

// >>> def logist(x, loc, scale):

// ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)

// >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\

// ... logist(bins, loc, scale).max())

// >>> plt.show()

//

#if TODO

given= def logist(x, loc, scale):;

var expected=

"... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)";

Assert.AreEqual(expected, given.repr);

given= plt.plot(bins, logist(bins, loc, scale)*count.max()/\;

expected=

"... logist(bins, loc, scale).max())";

Assert.AreEqual(expected, given.repr);

given= plt.show();

#endif

}

[TestMethod]

public void lognormalTest()

{

// Draw samples from the distribution:

// >>> mu, sigma = 3., 1. # mean and standard deviation

// >>> s = np.random.lognormal(mu, sigma, 1000)

//

#if TODO

var given= mu, sigma = 3., 1. # mean and standard deviation;

given= s = np.random.lognormal(mu, sigma, 1000);

#endif

// Display the histogram of the samples, along with

// the probability density function:

// >>> import matplotlib.pyplot as plt

// >>> count, bins, ignored = plt.hist(s, 100, density=True, align='mid')

//

#if TODO

given= import matplotlib.pyplot as plt;

given= count, bins, ignored = plt.hist(s, 100, density=True, align='mid');

#endif

// >>> x = np.linspace(min(bins), max(bins), 10000)

// >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))

// ... / (x * sigma * np.sqrt(2 * np.pi)))

//

#if TODO

given= x = np.linspace(min(bins), max(bins), 10000);

given= pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2));

var expected=

"... / (x * sigma * np.sqrt(2 * np.pi)))";

Assert.AreEqual(expected, given.repr);

#endif

// >>> plt.plot(x, pdf, linewidth=2, color='r')

// >>> plt.axis('tight')

// >>> plt.show()

//

#if TODO

given= plt.plot(x, pdf, linewidth=2, color='r');

given= plt.axis('tight');

given= plt.show();

#endif

// Demonstrate that taking the products of random samples from a uniform

// distribution can be fit well by a log-normal probability density

// function.

// >>> # Generate a thousand samples: each is the product of 100 random

// >>> # values, drawn from a normal distribution.

// >>> b = []

// >>> for i in range(1000):

// ... a = 10. + np.random.random(100)

// ... b.append(np.product(a))

//

#if TODO

given= # Generate a thousand samples: each is the product of 100 random;

given= # values, drawn from a normal distribution.;

given= b = [];

given= for i in range(1000):;

expected=

"... a = 10. + np.random.random(100)\n" +

"... b.append(np.product(a))";

Assert.AreEqual(expected, given.repr);

#endif

// >>> b = np.array(b) / np.min(b) # scale values to be positive

// >>> count, bins, ignored = plt.hist(b, 100, density=True, align='mid')

// >>> sigma = np.std(np.log(b))

// >>> mu = np.mean(np.log(b))

//

#if TODO

given= b = np.array(b) / np.min(b) # scale values to be positive;

given= count, bins, ignored = plt.hist(b, 100, density=True, align='mid');

given= sigma = np.std(np.log(b));

given= mu = np.mean(np.log(b));

#endif

// >>> x = np.linspace(min(bins), max(bins), 10000)

// >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))

// ... / (x * sigma * np.sqrt(2 * np.pi)))

//