random.py 2.8 KB
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# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
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#
# Copyright(C) 2013-2017 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
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from builtins import object
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import numpy as np
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from functools import reduce
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class Random(object):
    @staticmethod
    def pm1(dtype=np.dtype('int'), shape=1):

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        size = int(reduce(lambda x, y: x*y, shape))
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        if issubclass(dtype.type, np.complexfloating):
            x = np.array([1 + 0j, 0 + 1j, -1 + 0j, 0 - 1j], dtype=dtype)
            x = x[np.random.randint(4, high=None, size=size)]
        else:
            x = 2 * np.random.randint(2, high=None, size=size) - 1

        return x.astype(dtype).reshape(shape)

    @staticmethod
    def normal(dtype=np.dtype('float64'), shape=(1,), mean=None, std=None):

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        size = int(reduce(lambda x, y: x*y, shape))
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        if issubclass(dtype.type, np.complexfloating):
            x = np.empty(size, dtype=dtype)
            x.real = np.random.normal(loc=0, scale=np.sqrt(0.5), size=size)
            x.imag = np.random.normal(loc=0, scale=np.sqrt(0.5), size=size)
        else:
            x = np.random.normal(loc=0, scale=1, size=size)
            x = x.astype(dtype, copy=False)

        x = x.reshape(shape)

        if std is not None:
            x *= dtype.type(std)

        if mean is not None:
            x += dtype.type(mean)

        return x

    @staticmethod
    def uniform(dtype=np.dtype('float64'), shape=1, low=0, high=1):

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        size = int(reduce(lambda x, y: x*y, shape))
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        if issubclass(dtype.type, np.complexfloating):
            x = np.empty(size, dtype=dtype)
            x.real = (high - low) * np.random.random(size=size) + low
            x.imag = (high - low) * np.random.random(size=size) + low
        elif dtype in [np.dtype('int8'), np.dtype('int16'), np.dtype('int32'),
                       np.dtype('int64')]:
            x = np.random.random_integers(min(low, high),
                                          high=max(low, high),
                                          size=size)
        else:
            x = (high - low) * np.random.random(size=size) + low

        return x.astype(dtype, copy=False).reshape(shape)