def gumbel(shape, dtype=default_override_or(np.float32), loc=0.0, scale=1.0, seed=auto_select, name=''): """gumbel(shape, dtype=default_override_or(np.float32), loc=0.0, scale=1.0, seed=auto_select, name='') Generates samples from the Gumbel distribution with location `loc` and scale `scale`. Args: shape (tuple): shape of the output (entries are independent random draws) dtype (np.float32 or np.float64): data type. Default is np.float32. loc (float): location of the distribution scale (float): scale of the distribution seed (int): pseudo random number generator seed (default: automatically select a unique seed) name (str, optional): the name of the Function instance in the network Returns: :class:`~cntk.ops.functions.Function` Examples: >>> g = C.random.gumbel((2,3), seed=98052) >>> g.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code array([[-0.987713, -0.522298, 0.425918], [-1.019599, 5.435177, 1.586071]], dtype=float32) See also: `The Gumbel-Max Trick <https://hips.seas.harvard.edu/blog/2013/04/06/the-gumbel-max-trick-for-discrete-distributions/>`_. """ from cntk.cntk_py import gumbel_random shape, dtype = sanitize_random_args(shape, dtype) return gumbel_random(shape, dtype, loc, scale, seed, name)
def uniform(shape, dtype=default_override_or(np.float32), low=0.0, high=1.0, seed=auto_select, name=''): """uniform(shape, dtype=default_override_or(np.float32), low=0.0, high=1.0, seed=auto_select, name='') Generates samples from the uniform distribution in the interval [`low`,`high`). Args: shape (tuple): shape of the output (entries are independent random draws) dtype (np.float32 or np.float64): data type. Default is np.float32. low (float): lower end of the range of the random numbers high (float): upper end of the range of the random numbers seed (int): pseudo random number generator seed (default: automatically select a unique seed) name (str, optional): the name of the Function instance in the network Returns: :class:`~cntk.ops.functions.Function` Examples: >>> u = C.random.uniform((2,3), seed=98052) >>> u.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code array([[ 0.931785, 0.814722, 0.479606], [ 0.937468, 0.004351, 0.185131]], dtype=float32) """ from cntk.cntk_py import uniform_random shape, dtype = sanitize_random_args(shape, dtype) return uniform_random(shape, dtype, low, high, seed, name)
def gumbel(shape, dtype=default_override_or(np.float32), loc=0.0, scale=1.0, seed=auto_select, name=''): """gumbel(shape, dtype=default_override_or(np.float32), loc=0.0, scale=1.0, seed=auto_select, name='') Generates samples from the Gumbel distribution with location `loc` and scale `scale`. Args: shape (tuple): shape of the output (entries are independent random draws) dtype (np.float32 or np.float64): data type. Default is np.float32. loc (float): location of the distribution scale (float): scale of the distribution seed (int): pseudo random number generator seed (default: automatically select a unique seed) name (str, optional): the name of the Function instance in the network Returns: :class:`~cntk.ops.functions.Function` Examples: >>> g = C.random.gumbel((2,3), seed=98052) >>> g.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code array([[-0.987713, -0.522298, 0.425918], [-1.019599, 5.435177, 1.586071]], dtype=float32) See also: `The Gumbel-Max Trick <https://hips.seas.harvard.edu/blog/2013/04/06/the-gumbel-max-trick-for-discrete-distributions/>`_. """ from cntk.cntk_py import gumbel_random shape, dtype = sanitize_random_args(shape, dtype) return gumbel_random(shape, dtype, loc, scale, seed, name)
def normal(shape, dtype=default_override_or(np.float32), mean=0.0, scale=1.0, seed=auto_select, name=''): """normal(shape, dtype=default_override_or(np.float32), mean=0.0, scale=1.0, seed=auto_select, name='') Generates samples from the normal distribution with mean `mean` and standard deviation `scale`. Args: shape (tuple): shape of the output (entries are independent random draws) dtype (np.float32 or np.float64): data type. Default is np.float32. mean (float): mean of the distribution scale (float): scale (standard deviation) of the distribution seed (int): pseudo random number generator seed (default: automatically select a unique seed) name (str, optional): the name of the Function instance in the network Returns: :class:`~cntk.ops.functions.Function` Examples: >>> z = C.random.normal((2,3), seed=98052) >>> z.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code array([[ 1.803254, 0.995395, -0.631974], [-1.73672 , 0.005615, -0.340025]], dtype=float32) """ from cntk.cntk_py import normal_random shape, dtype = sanitize_random_args(shape, dtype) return normal_random(shape, dtype, mean, scale, seed, name)
def uniform(shape, dtype=default_override_or(np.float32), low=0.0, high=1.0, seed=auto_select, name=''): """uniform(shape, dtype=default_override_or(np.float32), low=0.0, high=1.0, seed=auto_select, name='') Generates samples from the uniform distribution in the interval [`low`,`high`). Args: shape (tuple): shape of the output (entries are independent random draws) dtype (np.float32 or np.float64): data type. Default is np.float32. low (float): lower end of the range of the random numbers high (float): upper end of the range of the random numbers seed (int): pseudo random number generator seed (default: automatically select a unique seed) name (str, optional): the name of the Function instance in the network Returns: :class:`~cntk.ops.functions.Function` Examples: >>> u = C.random.uniform((2,3), seed=98052) >>> u.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code array([[ 0.931785, 0.814722, 0.479606], [ 0.937468, 0.004351, 0.185131]], dtype=float32) """ from cntk.cntk_py import uniform_random shape, dtype = sanitize_random_args(shape, dtype) return uniform_random(shape, dtype, low, high, seed, name)
def bernoulli(shape, dtype=default_override_or(np.float32), mean=0.5, seed=auto_select, name=''): """bernoulli(shape, dtype=default_override_or(np.float32), mean=0.5, seed=auto_select, name='') Generates samples from the Bernoulli distribution with success probability `mean`. Args: shape (tuple): shape of the output (entries are independent random draws) dtype (np.float32 or np.float64): data type. Default is np.float32. mean (float): success probability seed (int): pseudo random number generator seed (default: automatically select a unique seed) name (str, optional): the name of the Function instance in the network Returns: :class:`~cntk.ops.functions.Function` Examples: >>> b = C.random.bernoulli((2,3), seed=98052) >>> b.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code array([[ 1., 1., 0.], [ 1., 0., 0.]], dtype=float32) """ from cntk.cntk_py import bernoulli_random shape, dtype = sanitize_random_args(shape, dtype) return bernoulli_random(shape, dtype, mean, seed, name)
def bernoulli(shape, dtype=default_override_or(np.float32), mean=0.5, seed=auto_select, name=''): """bernoulli(shape, dtype=default_override_or(np.float32), mean=0.5, seed=auto_select, name='') Generates samples from the Bernoulli distribution with success probability `mean`. Args: shape (tuple): shape of the output (entries are independent random draws) dtype (np.float32 or np.float64): data type. Default is np.float32. mean (float): success probability seed (int): pseudo random number generator seed (default: automatically select a unique seed) name (str, optional): the name of the Function instance in the network Returns: :class:`~cntk.ops.functions.Function` Examples: >>> b = C.random.bernoulli((2,3), seed=98052) >>> b.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code array([[ 1., 1., 0.], [ 1., 0., 0.]], dtype=float32) """ from cntk.cntk_py import bernoulli_random shape, dtype = sanitize_random_args(shape, dtype) return bernoulli_random(shape, dtype, mean, seed, name)
def normal(shape, dtype=default_override_or(np.float32), mean=0.0, scale=1.0, seed=auto_select, name=''): """normal(shape, dtype=default_override_or(np.float32), mean=0.0, scale=1.0, seed=auto_select, name='') Generates samples from the normal distribution with mean `mean` and standard deviation `scale`. Args: shape (tuple): shape of the output (entries are independent random draws) dtype (np.float32 or np.float64): data type. Default is np.float32. mean (float): mean of the distribution scale (float): scale (standard deviation) of the distribution seed (int): pseudo random number generator seed (default: automatically select a unique seed) name (str, optional): the name of the Function instance in the network Returns: :class:`~cntk.ops.functions.Function` Examples: >>> z = C.random.normal((2,3), seed=98052) >>> z.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code array([[ 1.803254, 0.995395, -0.631974], [-1.73672 , 0.005615, -0.340025]], dtype=float32) """ from cntk.cntk_py import normal_random shape, dtype = sanitize_random_args(shape, dtype) return normal_random(shape, dtype, mean, scale, seed, name)