# Backward compatible module for RandomArray __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, = 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()