numpy.random

All Samples(289) | Call(1) | Derive(0) | Import(288)

========================
Random Number Generation
========================

==================== =========================================================
Utility functions
==============================================================================
random               Uniformly distributed values of a given shape.
bytes                Uniformly distributed random bytes.
random_integers      Uniformly distributed integers in a given range.
random_sample        Uniformly distributed floats in a given range.
permutation          Randomly permute a sequence / generate a random sequence.
shuffle              Randomly permute a sequence in place.
seed                 Seed the random number generator.
==================== =========================================================

==================== =========================================================
Compatibility functions
==============================================================================
rand                 Uniformly distributed values.
randn                Normally distributed values.
ranf                 Uniformly distributed floating point numbers.
randint              Uniformly distributed integers in a given range.
==================== =========================================================

==================== =========================================================
Univariate distributions
==============================================================================
beta                 Beta distribution over ``[0, 1]``.
binomial             Binomial distribution.
chisquare            :math:`\chi^2` distribution.
exponential          Exponential distribution.
f                    F (Fisher-Snedecor) distribution.
gamma                Gamma distribution.
geometric            Geometric distribution.
gumbel               Gumbel distribution.
hypergeometric       Hypergeometric distribution.
laplace              Laplace distribution.
logistic             Logistic distribution.
lognormal            Log-normal distribution.
logseries            Logarithmic series distribution.
negative_binomial    Negative binomial distribution.
noncentral_chisquare Non-central chi-square distribution.
noncentral_f         Non-central F distribution.
normal               Normal / Gaussian distribution.
pareto               Pareto distribution.
poisson              Poisson distribution.
power                Power distribution.
rayleigh             Rayleigh distribution.
triangular           Triangular distribution.
uniform              Uniform distribution.
vonmises             Von Mises circular distribution.
wald                 Wald (inverse Gaussian) distribution.
weibull              Weibull distribution.
zipf                 Zipf's distribution over ranked data.
==================== =========================================================

==================== =========================================================
Multivariate distributions
==============================================================================
dirichlet            Multivariate generalization of Beta distribution.
multinomial          Multivariate generalization of the binomial distribution.
multivariate_normal  Multivariate generalization of the normal distribution.
==================== =========================================================

==================== =========================================================
Standard distributions
==============================================================================
standard_cauchy      Standard Cauchy-Lorentz distribution.
standard_exponential Standard exponential distribution.
standard_gamma       Standard Gamma distribution.
standard_normal      Standard normal distribution.
standard_t           Standard Student's t-distribution.
==================== =========================================================

==================== =========================================================
Internal functions
==============================================================================
get_state            Get tuple representing internal state of generator.
set_state            Set state of generator.
==================== =========================================================

src/p/y/pyfusion-HEAD/examples/test_savez.py pyfusion(Download)

#    debug_save_compress=False;
 
global verbose
from numpy import savez, array, arange, remainder, mod, sin, pi, min, max, \
        size, diff, random, mean, unique, sort, sqrt, float32
from time import time
from pylab import plot, show

src/n/i/nipy-HEAD/examples/neurospin/bayesian_gaussian_mixtures.py nipy(Download)

print __doc__
 
import numpy as np
import numpy.random as nr
import pylab as pl
 
import nipy.neurospin.clustering.bgmm as bgmm

src/n/i/NiPy-OLD-HEAD/examples/neurospin/bayesian_gaussian_mixtures.py NiPy-OLD(Download)

print __doc__
 
import numpy as np
import numpy.random as nr
import pylab as pl
 
import nipy.neurospin.clustering.bgmm as bgmm

src/p/y/pysparse-HEAD/trunk/examples/jdsym_test.py pysparse(Download)

from pysparse.sparse import spmatrix
from pysparse.eigen import jdsym
from pysparse.itsolvers.krylov import qmrs
from numpy import zeros, dot, allclose, multiply, random
from math import sqrt
 
class diagPrecShifted:

src/p/y/pysparse-HEAD/examples/jdsym_test.py pysparse(Download)

from pysparse.sparse import spmatrix
from pysparse.eigen import jdsym
from pysparse.itsolvers.krylov import qmrs
from numpy import zeros, dot, allclose, multiply, random
from math import sqrt
 
class diagPrecShifted:

src/n/i/nipy-HEAD/examples/neurospin/clustering_comparisons.py nipy(Download)

print __doc__
 
import numpy as np
import numpy.random as nr
import nipy.neurospin.graph.field as ff

src/p/y/pysparse-1.1.1-dev/examples/jdsym_test.py pysparse(Download)

from pysparse.sparse import spmatrix
from pysparse.eigen import jdsym
from pysparse.itsolvers.krylov import qmrs
from numpy import zeros, dot, allclose, multiply, random
from math import sqrt
 
class diagPrecShifted:

src/n/i/NiPy-OLD-HEAD/examples/neurospin/clustering_comparisons.py NiPy-OLD(Download)

"""
 
import numpy as np
import numpy.random as nr
import nipy.neurospin.graph.field as ff

src/s/c/scikits.statsmodels-0.2.0/scikits/statsmodels/sandbox/examples/example_gam.py scikits.statsmodels(Download)

example = 3  # 1,2 or 3
 
import numpy as np
import numpy.random as R
from scikits.statsmodels.sandbox.gam import AdditiveModel
from scikits.statsmodels.sandbox.gam import Model as GAM #?
from scikits.statsmodels.family import family

src/o/p/openrave-HEAD/trunk/python/examples/tutorial_iksolutions.py openrave(Download)

    from openravepy import *
else:
    from openravepy import OpenRAVEModel, OpenRAVEGlobalArguments
from numpy import random, array, linspace
from optparse import OptionParser
import time

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