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Diffstat (limited to 'numpy/oldnumeric/random_array.py')
| -rw-r--r-- | numpy/oldnumeric/random_array.py | 269 |
1 files changed, 0 insertions, 269 deletions
diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py deleted file mode 100644 index c43a49cdb..000000000 --- a/numpy/oldnumeric/random_array.py +++ /dev/null @@ -1,269 +0,0 @@ -"""Backward compatible module for RandomArray - -""" -from __future__ import division, absolute_import, print_function - -__all__ = ['ArgumentError', 'F', 'beta', 'binomial', 'chi_square', 'exponential', - 'gamma', 'get_seed', 'mean_var_test', 'multinomial', - 'multivariate_normal', 'negative_binomial', 'noncentral_F', - 'noncentral_chi_square', 'normal', 'permutation', 'poisson', - 'randint', 'random', 'random_integers', 'seed', 'standard_normal', - 'uniform'] - -ArgumentError = ValueError - -import numpy.random.mtrand as mt -import numpy as np - -def seed(x=0, y=0): - if (x == 0 or y == 0): - mt.seed() - else: - mt.seed((x, y)) - -def get_seed(): - raise NotImplementedError( - "If you want to save the state of the random number generator.\n" - "Then you should use obj = numpy.random.get_state() followed by.\n" - "numpy.random.set_state(obj).") - -def random(shape=[]): - "random(n) or random([n, m, ...]) returns array of random numbers" - if shape == []: - shape = None - return mt.random_sample(shape) - -def uniform(minimum, maximum, shape=[]): - """uniform(minimum, maximum, shape=[]) returns array of given shape of random reals - in given range""" - if shape == []: - shape = None - return mt.uniform(minimum, maximum, shape) - -def randint(minimum, maximum=None, shape=[]): - """randint(min, max, shape=[]) = random integers >=min, < max - If max not given, random integers >= 0, <min""" - if not isinstance(minimum, int): - raise ArgumentError("randint requires first argument integer") - if maximum is None: - maximum = minimum - minimum = 0 - if not isinstance(maximum, int): - raise ArgumentError("randint requires second argument integer") - a = ((maximum-minimum)* random(shape)) - if isinstance(a, np.ndarray): - return minimum + a.astype(np.int) - else: - return minimum + int(a) - -def random_integers(maximum, minimum=1, shape=[]): - """random_integers(max, min=1, shape=[]) = random integers in range min-max inclusive""" - return randint(minimum, maximum+1, shape) - -def permutation(n): - "permutation(n) = a permutation of indices range(n)" - return mt.permutation(n) - -def standard_normal(shape=[]): - """standard_normal(n) or standard_normal([n, m, ...]) returns array of - random numbers normally distributed with mean 0 and standard - deviation 1""" - if shape == []: - shape = None - return mt.standard_normal(shape) - -def normal(mean, std, shape=[]): - """normal(mean, std, n) or normal(mean, std, [n, m, ...]) returns - array of random numbers randomly distributed with specified mean and - standard deviation""" - if shape == []: - shape = None - return mt.normal(mean, std, shape) - -def multivariate_normal(mean, cov, shape=[]): - """multivariate_normal(mean, cov) or multivariate_normal(mean, cov, [m, n, ...]) - returns an array containing multivariate normally distributed random numbers - with specified mean and covariance. - - mean must be a 1 dimensional array. cov must be a square two dimensional - array with the same number of rows and columns as mean has elements. - - The first form returns a single 1-D array containing a multivariate - normal. - - The second form returns an array of shape (m, n, ..., cov.shape[0]). - In this case, output[i,j,...,:] is a 1-D array containing a multivariate - normal.""" - if shape == []: - shape = None - return mt.multivariate_normal(mean, cov, shape) - -def exponential(mean, shape=[]): - """exponential(mean, n) or exponential(mean, [n, m, ...]) returns array - of random numbers exponentially distributed with specified mean""" - if shape == []: - shape = None - return mt.exponential(mean, shape) - -def beta(a, b, shape=[]): - """beta(a, b) or beta(a, b, [n, m, ...]) returns array of beta distributed random numbers.""" - if shape == []: - shape = None - return mt.beta(a, b, shape) - -def gamma(a, r, shape=[]): - """gamma(a, r) or gamma(a, r, [n, m, ...]) returns array of gamma distributed random numbers.""" - if shape == []: - shape = None - return mt.gamma(a, r, shape) - -def F(dfn, dfd, shape=[]): - """F(dfn, dfd) or F(dfn, dfd, [n, m, ...]) returns array of F distributed random numbers with dfn degrees of freedom in the numerator and dfd degrees of freedom in the denominator.""" - if shape == []: - shape = None - return mt.f(dfn, dfd, shape) - -def noncentral_F(dfn, dfd, nconc, shape=[]): - """noncentral_F(dfn, dfd, nonc) or noncentral_F(dfn, dfd, nonc, [n, m, ...]) returns array of noncentral F distributed random numbers with dfn degrees of freedom in the numerator and dfd degrees of freedom in the denominator, and noncentrality parameter nconc.""" - if shape == []: - shape = None - return mt.noncentral_f(dfn, dfd, nconc, shape) - -def chi_square(df, shape=[]): - """chi_square(df) or chi_square(df, [n, m, ...]) returns array of chi squared distributed random numbers with df degrees of freedom.""" - if shape == []: - shape = None - return mt.chisquare(df, shape) - -def noncentral_chi_square(df, nconc, shape=[]): - """noncentral_chi_square(df, nconc) or chi_square(df, nconc, [n, m, ...]) returns array of noncentral chi squared distributed random numbers with df degrees of freedom and noncentrality parameter.""" - if shape == []: - shape = None - return mt.noncentral_chisquare(df, nconc, shape) - -def binomial(trials, p, shape=[]): - """binomial(trials, p) or binomial(trials, p, [n, m, ...]) returns array of binomially distributed random integers. - - trials is the number of trials in the binomial distribution. - p is the probability of an event in each trial of the binomial distribution.""" - if shape == []: - shape = None - return mt.binomial(trials, p, shape) - -def negative_binomial(trials, p, shape=[]): - """negative_binomial(trials, p) or negative_binomial(trials, p, [n, m, ...]) returns - array of negative binomially distributed random integers. - - trials is the number of trials in the negative binomial distribution. - p is the probability of an event in each trial of the negative binomial distribution.""" - if shape == []: - shape = None - return mt.negative_binomial(trials, p, shape) - -def multinomial(trials, probs, shape=[]): - """multinomial(trials, probs) or multinomial(trials, probs, [n, m, ...]) returns - array of multinomial distributed integer vectors. - - trials is the number of trials in each multinomial distribution. - probs is a one dimensional array. There are len(prob)+1 events. - prob[i] is the probability of the i-th event, 0<=i<len(prob). - The probability of event len(prob) is 1.-np.sum(prob). - - The first form returns a single 1-D array containing one multinomially - distributed vector. - - The second form returns an array of shape (m, n, ..., len(probs)). - In this case, output[i,j,...,:] is a 1-D array containing a multinomially - distributed integer 1-D array.""" - if shape == []: - shape = None - return mt.multinomial(trials, probs, shape) - -def poisson(mean, shape=[]): - """poisson(mean) or poisson(mean, [n, m, ...]) returns array of poisson - distributed random integers with specified mean.""" - if shape == []: - shape = None - return mt.poisson(mean, shape) - - -def mean_var_test(x, type, mean, var, skew=[]): - n = len(x) * 1.0 - x_mean = np.sum(x, axis=0)/n - x_minus_mean = x - x_mean - x_var = np.sum(x_minus_mean*x_minus_mean, axis=0)/(n-1.0) - print("\nAverage of ", len(x), type) - print("(should be about ", mean, "):", x_mean) - print("Variance of those random numbers (should be about ", var, "):", x_var) - if skew != []: - x_skew = (np.sum(x_minus_mean*x_minus_mean*x_minus_mean, axis=0)/9998.)/x_var**(3./2.) - print("Skewness of those random numbers (should be about ", skew, "):", x_skew) - -def test(): - obj = mt.get_state() - mt.set_state(obj) - obj2 = mt.get_state() - if (obj2[1] - obj[1]).any(): - raise SystemExit("Failed seed test.") - print("First random number is", random()) - print("Average of 10000 random numbers is", np.sum(random(10000), axis=0)/10000.) - x = random([10, 1000]) - if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000: - raise SystemExit("random returned wrong shape") - x.shape = (10000,) - print("Average of 100 by 100 random numbers is", np.sum(x, axis=0)/10000.) - y = uniform(0.5, 0.6, (1000, 10)) - if len(y.shape) !=2 or y.shape[0] != 1000 or y.shape[1] != 10: - raise SystemExit("uniform returned wrong shape") - y.shape = (10000,) - if np.minimum.reduce(y) <= 0.5 or np.maximum.reduce(y) >= 0.6: - raise SystemExit("uniform returned out of desired range") - print("randint(1, 10, shape=[50])") - print(randint(1, 10, shape=[50])) - print("permutation(10)", permutation(10)) - print("randint(3,9)", randint(3, 9)) - print("random_integers(10, shape=[20])") - print(random_integers(10, shape=[20])) - s = 3.0 - x = normal(2.0, s, [10, 1000]) - if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000: - raise SystemExit("standard_normal returned wrong shape") - x.shape = (10000,) - mean_var_test(x, "normally distributed numbers with mean 2 and variance %f"%(s**2,), 2, s**2, 0) - x = exponential(3, 10000) - mean_var_test(x, "random numbers exponentially distributed with mean %f"%(s,), s, s**2, 2) - x = multivariate_normal(np.array([10, 20]), np.array(([1, 2], [2, 4]))) - print("\nA multivariate normal", x) - if x.shape != (2,): raise SystemExit("multivariate_normal returned wrong shape") - x = multivariate_normal(np.array([10, 20]), np.array([[1, 2], [2, 4]]), [4, 3]) - print("A 4x3x2 array containing multivariate normals") - print(x) - if x.shape != (4, 3, 2): raise SystemExit("multivariate_normal returned wrong shape") - x = multivariate_normal(np.array([-100, 0, 100]), np.array([[3, 2, 1], [2, 2, 1], [1, 1, 1]]), 10000) - x_mean = np.sum(x, axis=0)/10000. - print("Average of 10000 multivariate normals with mean [-100,0,100]") - print(x_mean) - x_minus_mean = x - x_mean - print("Estimated covariance of 10000 multivariate normals with covariance [[3,2,1],[2,2,1],[1,1,1]]") - print(np.dot(np.transpose(x_minus_mean), x_minus_mean)/9999.) - x = beta(5.0, 10.0, 10000) - mean_var_test(x, "beta(5.,10.) random numbers", 0.333, 0.014) - x = gamma(.01, 2., 10000) - mean_var_test(x, "gamma(.01,2.) random numbers", 2*100, 2*100*100) - x = chi_square(11., 10000) - mean_var_test(x, "chi squared random numbers with 11 degrees of freedom", 11, 22, 2*np.sqrt(2./11.)) - x = F(5., 10., 10000) - mean_var_test(x, "F random numbers with 5 and 10 degrees of freedom", 1.25, 1.35) - x = poisson(50., 10000) - mean_var_test(x, "poisson random numbers with mean 50", 50, 50, 0.14) - print("\nEach element is the result of 16 binomial trials with probability 0.5:") - print(binomial(16, 0.5, 16)) - print("\nEach element is the result of 16 negative binomial trials with probability 0.5:") - print(negative_binomial(16, 0.5, [16,])) - print("\nEach row is the result of 16 multinomial trials with probabilities [0.1, 0.5, 0.1 0.3]:") - x = multinomial(16, [0.1, 0.5, 0.1], 8) - print(x) - print("Mean = ", np.sum(x, axis=0)/8.) - -if __name__ == '__main__': - test() |