Added documenntation to the docs folder and edited the mac bash script in response to tests
This commit is contained in:
Slater-Victoroff
266
numpy-1.6.2/numpy/oldnumeric/random_array.py
Normal file
266
numpy-1.6.2/numpy/oldnumeric/random_array.py
Normal file
@@ -0,0 +1,266 @@
|
||||
# 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, <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()
|
||||
Reference in New Issue
Block a user