Version 1.0b1 API Documentation generated by Endo 2006-08-14

Variables

• ArgumentError

  ArgumentError = ValueError
  
  

Function summary

• F(dfn, dfd, shape = [ ])
• beta(a, b, shape = [ ])
• binomial(trials, p, shape = [ ])
• chi_square(df, shape = [ ])
• exponential(mean, shape = [ ])
• gamma(a, r, shape = [ ])
• get_seed()
• mean_var_test(x, type, mean, var, skew = [ ])
• multinomial(trials, probs, shape = [ ])
• multivariate_normal(mean, cov, shape = [ ])
• negative_binomial(trials, p, shape = [ ])
• noncentral_F(dfn, dfd, nconc, shape = [ ])
• noncentral_chi_square(df, nconc, shape = [ ])
• normal(mean, std, shape = [ ])
• permutation(n)
• poisson(mean, shape = [ ])
• randint(minimum, maximum = None, shape = [ ])
• random(shape = [ ])
• random_integers(maximum, minimum = 1, shape = [ ])
• seed(x = 0, y = 0)
• standard_normal(shape = [ ])
• test()
• uniform(minimum, maximum, shape = [ ])

Functions

• 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.

• beta(a, b, shape = [ ])

  beta(a, b) or beta(a, b, [n, m, ...]) returns array of beta distributed random numbers.

• 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.

• 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.

• exponential(mean, shape = [ ])

  exponential(mean, n) or exponential(mean, [n, m, ...]) returns array of random numbers exponentially distributed with specified mean

• gamma(a, r, shape = [ ])

  gamma(a, r) or gamma(a, r, [n, m, ...]) returns array of gamma distributed random numbers.

• get_seed()
• mean_var_test(x, type, mean, var, skew = [ ])
• 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.-Numeric.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.

• 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.

• 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.

• 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.

• 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.

• 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

• permutation(n)

  permutation(n) = a permutation of indices range(n)

• poisson(mean, shape = [ ])

  poisson(mean) or poisson(mean, [n, m, ...]) returns array of poisson distributed random integers with specified mean.

• randint(minimum, maximum = None, shape = [ ])

  randint(min, max, shape=[]) = random integers >=min, < max If max not given, random integers >= 0, <min

• random(shape = [ ])

  random(n) or random([n, m, ...]) returns array of random numbers

• random_integers(maximum, minimum = 1, shape = [ ])

  random_integers(max, min=1, shape=[]) = random integers in range min-max inclusive

• seed(x = 0, y = 0)
• 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

• test()
• uniform(minimum, maximum, shape = [ ])

  uniform(minimum, maximum, shape=[]) returns array of given shape of random reals in given range

Imported names