mxnet.symbol.gen_random

Functions

binomial([n, p, shape, ctx, dtype, name, ...])

Draw random samples from a binomial distribution.

exponential([lam, shape, ctx, dtype, name, ...])

Draw random samples from an exponential distribution.

exponential_like([data, lam, name, attr, out])

Draw random samples from an exponential distribution according to the input array shape.

gamma([alpha, beta, shape, ctx, dtype, ...])

Draw random samples from a gamma distribution.

gamma_like([data, alpha, beta, name, attr, out])

Draw random samples from a gamma distribution according to the input array shape.

generalized_negative_binomial([mu, alpha, ...])

Draw random samples from a generalized negative binomial distribution.

generalized_negative_binomial_like([data, ...])

Draw random samples from a generalized negative binomial distribution according to the input array shape.

negative_binomial([k, p, shape, ctx, dtype, ...])

Draw random samples from a negative binomial distribution.

negative_binomial_like([data, k, p, name, ...])

Draw random samples from a negative binomial distribution according to the input array shape.

normal([loc, scale, shape, ctx, dtype, ...])

Draw random samples from a normal (Gaussian) distribution.

normal_like([data, loc, scale, name, attr, out])

Draw random samples from a normal (Gaussian) distribution according to the input array shape.

pdf_dirichlet([sample, alpha, is_log, name, ...])

Computes the value of the PDF of sample of Dirichlet distributions with parameter alpha.

pdf_exponential([sample, lam, is_log, name, ...])

Computes the value of the PDF of sample of exponential distributions with parameters lam (rate).

pdf_gamma([sample, alpha, beta, is_log, ...])

Computes the value of the PDF of sample of gamma distributions with parameters alpha (shape) and beta (rate).

pdf_generalized_negative_binomial([sample, ...])

Computes the value of the PDF of sample of generalized negative binomial distributions with parameters mu (mean) and alpha (dispersion).

pdf_negative_binomial([sample, k, p, ...])

Computes the value of the PDF of samples of negative binomial distributions with parameters k (failure limit) and p (failure probability).

pdf_normal([sample, mu, sigma, is_log, ...])

Computes the value of the PDF of sample of normal distributions with parameters mu (mean) and sigma (standard deviation).

pdf_poisson([sample, lam, is_log, name, ...])

Computes the value of the PDF of sample of Poisson distributions with parameters lam (rate).

pdf_uniform([sample, low, high, is_log, ...])

Computes the value of the PDF of sample of uniform distributions on the intervals given by [low,high).

poisson([lam, shape, ctx, dtype, name, ...])

Draw random samples from a Poisson distribution.

poisson_like([data, lam, name, attr, out])

Draw random samples from a Poisson distribution according to the input array shape.

randint([low, high, shape, ctx, dtype, ...])

Draw random samples from a discrete uniform distribution.

uniform([low, high, shape, ctx, dtype, ...])

Draw random samples from a uniform distribution.

uniform_like([data, low, high, name, attr, out])

Draw random samples from a uniform distribution according to the input array shape.
mxnet.symbol.gen_random.binomial(n=_Null, p=_Null, shape=_Null, ctx=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a binomial distribution.

Samples are distributed according to a binomial distribution parametrized by n (number of experiments) and p (success probability in each experiment). Samples will always be returned as a floating point data type.

Example:

binomial(n=3, p=0.4, shape=(2,2)) = [[ 1.,  0.],
                                     [ 1.,  2.]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L166

Parameters:

  • n (int, optional, default='1') – number of experiments.

  • p (float, optional, default=1) – success probability in each experiment.

  • shape (Shape(tuple), optional, default=None) – Shape of the output.

  • ctx (string, optional, default='') – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.

  • dtype ({'None', 'bfloat16', 'float16', 'float32', 'float64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.exponential(lam=_Null, shape=_Null, ctx=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from an exponential distribution.

Samples are distributed according to an exponential distribution parametrized by lambda (rate).

Example:

exponential(lam=4, shape=(2,2)) = [[ 0.0097189 ,  0.08999364],
                                   [ 0.04146638,  0.31715935]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L138

Parameters:

  • lam (float, optional, default=1) – Lambda parameter (rate) of the exponential distribution.

  • shape (Shape(tuple), optional, default=None) – Shape of the output.

  • ctx (string, optional, default='') – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.

  • dtype ({'None', 'bfloat16', 'float16', 'float32', 'float64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.exponential_like(data=None, lam=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from an exponential distribution according to the input array shape.

Samples are distributed according to an exponential distribution parametrized by lambda (rate).

Example:

exponential(lam=4, data=ones(2,2)) = [[ 0.0097189 ,  0.08999364],
                                      [ 0.04146638,  0.31715935]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L263

Parameters:

  • lam (float, optional, default=1) – Lambda parameter (rate) of the exponential distribution.

  • data (Symbol) – The input

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.gamma(alpha=_Null, beta=_Null, shape=_Null, ctx=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a gamma distribution.

Samples are distributed according to a gamma distribution parametrized by alpha (shape) and beta (scale).

Example:

gamma(alpha=9, beta=0.5, shape=(2,2)) = [[ 7.10486984,  3.37695289],
                                         [ 3.91697288,  3.65933681]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L126

Parameters:

  • alpha (float, optional, default=1) – Alpha parameter (shape) of the gamma distribution.

  • beta (float, optional, default=1) – Beta parameter (scale) of the gamma distribution.

  • shape (Shape(tuple), optional, default=None) – Shape of the output.

  • ctx (string, optional, default='') – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.

  • dtype ({'None', 'bfloat16', 'float16', 'float32', 'float64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.gamma_like(data=None, alpha=_Null, beta=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a gamma distribution according to the input array shape.

Samples are distributed according to a gamma distribution parametrized by alpha (shape) and beta (scale).

Example:

gamma(alpha=9, beta=0.5, data=ones(2,2)) = [[ 7.10486984,  3.37695289],
                                            [ 3.91697288,  3.65933681]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L251

Parameters:

  • alpha (float, optional, default=1) – Alpha parameter (shape) of the gamma distribution.

  • beta (float, optional, default=1) – Beta parameter (scale) of the gamma distribution.

  • data (Symbol) – The input

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.generalized_negative_binomial(mu=_Null, alpha=_Null, shape=_Null, ctx=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a generalized negative binomial distribution.

Samples are distributed according to a generalized negative binomial distribution parametrized by mu (mean) and alpha (dispersion). alpha is defined as 1/k where k is the failure limit of the number of unsuccessful experiments (generalized to real numbers). Samples will always be returned as a floating point data type.

Example:

generalized_negative_binomial(mu=2.0, alpha=0.3, shape=(2,2)) = [[ 2.,  1.],
                                                                 [ 6.,  4.]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L195

Parameters:

  • mu (float, optional, default=1) – Mean of the negative binomial distribution.

  • alpha (float, optional, default=1) – Alpha (dispersion) parameter of the negative binomial distribution.

  • shape (Shape(tuple), optional, default=None) – Shape of the output.

  • ctx (string, optional, default='') – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.

  • dtype ({'None', 'bfloat16', 'float16', 'float32', 'float64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.generalized_negative_binomial_like(data=None, mu=_Null, alpha=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a generalized negative binomial distribution according to the input array shape.

Samples are distributed according to a generalized negative binomial distribution parametrized by mu (mean) and alpha (dispersion). alpha is defined as 1/k where k is the failure limit of the number of unsuccessful experiments (generalized to real numbers). Samples will always be returned as a floating point data type.

Example:

generalized_negative_binomial(mu=2.0, alpha=0.3, data=ones(2,2)) = [[ 2.,  1.],
                                                                    [ 6.,  4.]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L307

Parameters:

  • mu (float, optional, default=1) – Mean of the negative binomial distribution.

  • alpha (float, optional, default=1) – Alpha (dispersion) parameter of the negative binomial distribution.

  • data (Symbol) – The input

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.negative_binomial(k=_Null, p=_Null, shape=_Null, ctx=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a negative binomial distribution.

Samples are distributed according to a negative binomial distribution parametrized by k (limit of unsuccessful experiments) and p (failure probability in each experiment). Samples will always be returned as a floating point data type.

Example:

negative_binomial(k=3, p=0.4, shape=(2,2)) = [[ 4.,  7.],
                                              [ 2.,  5.]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L180

Parameters:

  • k (int, optional, default='1') – Limit of unsuccessful experiments.

  • p (float, optional, default=1) – Failure probability in each experiment.

  • shape (Shape(tuple), optional, default=None) – Shape of the output.

  • ctx (string, optional, default='') – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.

  • dtype ({'None', 'bfloat16', 'float16', 'float32', 'float64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.negative_binomial_like(data=None, k=_Null, p=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a negative binomial distribution according to the input array shape.

Samples are distributed according to a negative binomial distribution parametrized by k (limit of unsuccessful experiments) and p (failure probability in each experiment). Samples will always be returned as a floating point data type.

Example:

negative_binomial(k=3, p=0.4, data=ones(2,2)) = [[ 4.,  7.],
                                                 [ 2.,  5.]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L290

Parameters:

  • k (int, optional, default='1') – Limit of unsuccessful experiments.

  • p (float, optional, default=1) – Failure probability in each experiment.

  • data (Symbol) – The input

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.normal(loc=_Null, scale=_Null, shape=_Null, ctx=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a normal (Gaussian) distribution.

Note

The existing alias normal is deprecated.

Samples are distributed according to a normal distribution parametrized by loc (mean) and scale (standard deviation).

Example:

normal(loc=0, scale=1, shape=(2,2)) = [[ 1.89171135, -1.16881478],
                                       [-1.23474145,  1.55807114]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L114

Parameters:

  • loc (float, optional, default=0) – Mean of the distribution.

  • scale (float, optional, default=1) – Standard deviation of the distribution.

  • shape (Shape(tuple), optional, default=None) – Shape of the output.

  • ctx (string, optional, default='') – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.

  • dtype ({'None', 'bfloat16', 'float16', 'float32', 'float64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.normal_like(data=None, loc=_Null, scale=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a normal (Gaussian) distribution according to the input array shape.

Samples are distributed according to a normal distribution parametrized by loc (mean) and scale (standard deviation).

Example:

normal(loc=0, scale=1, data=ones(2,2)) = [[ 1.89171135, -1.16881478],
                                          [-1.23474145,  1.55807114]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L239

Parameters:

  • loc (float, optional, default=0) – Mean of the distribution.

  • scale (float, optional, default=1) – Standard deviation of the distribution.

  • data (Symbol) – The input

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.pdf_dirichlet(sample=None, alpha=None, is_log=_Null, name=None, attr=None, out=None, **kwargs)[source]

Computes the value of the PDF of sample of Dirichlet distributions with parameter alpha.

The shape of alpha must match the leftmost subshape of sample. That is, sample can have the same shape as alpha, in which case the output contains one density per distribution, or sample can be a tensor of tensors with that shape, in which case the output is a tensor of densities such that the densities at index i in the output are given by the samples at index i in sample parameterized by the value of alpha at index i.

Examples:

random_pdf_dirichlet(sample=[[1,2],[2,3],[3,4]], alpha=[2.5, 2.5]) =
    [38.413498, 199.60245, 564.56085]

sample = [[[1, 2, 3], [10, 20, 30], [100, 200, 300]],
          [[0.1, 0.2, 0.3], [0.01, 0.02, 0.03], [0.001, 0.002, 0.003]]]

random_pdf_dirichlet(sample=sample, alpha=[0.1, 0.4, 0.9]) =
    [[2.3257459e-02, 5.8420084e-04, 1.4674458e-05],
     [9.2589635e-01, 3.6860607e+01, 1.4674468e+03]]

Defined in /home/smola/mxnet/src/operator/random/pdf_op.cc:L355

Parameters:

  • sample (Symbol) – Samples from the distributions.

  • alpha (Symbol) – Concentration parameters of the distributions.

  • is_log (boolean, optional, default=0) – If set, compute the density of the log-probability instead of the probability.

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.pdf_exponential(sample=None, lam=None, is_log=_Null, name=None, attr=None, out=None, **kwargs)[source]

Computes the value of the PDF of sample of exponential distributions with parameters lam (rate).

The shape of lam must match the leftmost subshape of sample. That is, sample can have the same shape as lam, in which case the output contains one density per distribution, or sample can be a tensor of tensors with that shape, in which case the output is a tensor of densities such that the densities at index i in the output are given by the samples at index i in sample parameterized by the value of lam at index i.

Examples:

random_pdf_exponential(sample=[[1, 2, 3]], lam=[1]) =
    [[0.36787945, 0.13533528, 0.04978707]]

sample = [[1,2,3],
          [1,2,3],
          [1,2,3]]

random_pdf_exponential(sample=sample, lam=[1,0.5,0.25]) =
    [[0.36787945, 0.13533528, 0.04978707],
     [0.30326533, 0.18393973, 0.11156508],
     [0.1947002,  0.15163267, 0.11809164]]

Defined in /home/smola/mxnet/src/operator/random/pdf_op.cc:L329

Parameters:

  • sample (Symbol) – Samples from the distributions.

  • lam (Symbol) – Lambda (rate) parameters of the distributions.

  • is_log (boolean, optional, default=0) – If set, compute the density of the log-probability instead of the probability.

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.pdf_gamma(sample=None, alpha=None, beta=None, is_log=_Null, name=None, attr=None, out=None, **kwargs)[source]

Computes the value of the PDF of sample of gamma distributions with parameters alpha (shape) and beta (rate).

alpha and beta must have the same shape, which must match the leftmost subshape of sample. That is, sample can have the same shape as alpha and beta, in which case the output contains one density per distribution, or sample can be a tensor of tensors with that shape, in which case the output is a tensor of densities such that the densities at index i in the output are given by the samples at index i in sample parameterized by the values of alpha and beta at index i.

Examples:

random_pdf_gamma(sample=[[1,2,3,4,5]], alpha=[5], beta=[1]) =
    [[0.01532831, 0.09022352, 0.16803136, 0.19536681, 0.17546739]]

sample = [[1, 2, 3, 4, 5],
          [2, 3, 4, 5, 6],
          [3, 4, 5, 6, 7]]

random_pdf_gamma(sample=sample, alpha=[5,6,7], beta=[1,1,1]) =
    [[0.01532831, 0.09022352, 0.16803136, 0.19536681, 0.17546739],
     [0.03608941, 0.10081882, 0.15629345, 0.17546739, 0.16062315],
     [0.05040941, 0.10419563, 0.14622283, 0.16062315, 0.14900276]]

Defined in /home/smola/mxnet/src/operator/random/pdf_op.cc:L322

Parameters:

  • sample (Symbol) – Samples from the distributions.

  • alpha (Symbol) – Alpha (shape) parameters of the distributions.

  • is_log (boolean, optional, default=0) – If set, compute the density of the log-probability instead of the probability.

  • beta (Symbol) – Beta (scale) parameters of the distributions.

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.pdf_generalized_negative_binomial(sample=None, mu=None, alpha=None, is_log=_Null, name=None, attr=None, out=None, **kwargs)[source]

Computes the value of the PDF of sample of generalized negative binomial distributions with parameters mu (mean) and alpha (dispersion). This can be understood as a reparameterization of the negative binomial, where k = 1 / alpha and p = 1 / (mu * alpha + 1).

mu and alpha must have the same shape, which must match the leftmost subshape of sample. That is, sample can have the same shape as mu and alpha, in which case the output contains one density per distribution, or sample can be a tensor of tensors with that shape, in which case the output is a tensor of densities such that the densities at index i in the output are given by the samples at index i in sample parameterized by the values of mu and alpha at index i.

Examples:

random_pdf_generalized_negative_binomial(sample=[[1, 2, 3, 4]], alpha=[1], mu=[1]) =
    [[0.25, 0.125, 0.0625, 0.03125]]

sample = [[1,2,3,4],
          [1,2,3,4]]
random_pdf_generalized_negative_binomial(sample=sample, alpha=[1, 0.6666], mu=[1, 1.5]) =
    [[0.25,       0.125,      0.0625,     0.03125   ],
     [0.26517063, 0.16573331, 0.09667706, 0.05437994]]

Defined in /home/smola/mxnet/src/operator/random/pdf_op.cc:L348

Parameters:

  • sample (Symbol) – Samples from the distributions.

  • mu (Symbol) – Means of the distributions.

  • is_log (boolean, optional, default=0) – If set, compute the density of the log-probability instead of the probability.

  • alpha (Symbol) – Alpha (dispersion) parameters of the distributions.

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.pdf_negative_binomial(sample=None, k=None, p=None, is_log=_Null, name=None, attr=None, out=None, **kwargs)[source]

Computes the value of the PDF of samples of negative binomial distributions with parameters k (failure limit) and p (failure probability).

k and p must have the same shape, which must match the leftmost subshape of sample. That is, sample can have the same shape as k and p, in which case the output contains one density per distribution, or sample can be a tensor of tensors with that shape, in which case the output is a tensor of densities such that the densities at index i in the output are given by the samples at index i in sample parameterized by the values of k and p at index i.

Examples:

random_pdf_negative_binomial(sample=[[1,2,3,4]], k=[1], p=a[0.5]) =
    [[0.25, 0.125, 0.0625, 0.03125]]

# Note that k may be real-valued
sample = [[1,2,3,4],
          [1,2,3,4]]
random_pdf_negative_binomial(sample=sample, k=[1, 1.5], p=[0.5, 0.5]) =
    [[0.25,       0.125,      0.0625,     0.03125   ],
     [0.26516506, 0.16572815, 0.09667476, 0.05437956]]

Defined in /home/smola/mxnet/src/operator/random/pdf_op.cc:L341

Parameters:

  • sample (Symbol) – Samples from the distributions.

  • k (Symbol) – Limits of unsuccessful experiments.

  • is_log (boolean, optional, default=0) – If set, compute the density of the log-probability instead of the probability.

  • p (Symbol) – Failure probabilities in each experiment.

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.pdf_normal(sample=None, mu=None, sigma=None, is_log=_Null, name=None, attr=None, out=None, **kwargs)[source]

Computes the value of the PDF of sample of normal distributions with parameters mu (mean) and sigma (standard deviation).

mu and sigma must have the same shape, which must match the leftmost subshape of sample. That is, sample can have the same shape as mu and sigma, in which case the output contains one density per distribution, or sample can be a tensor of tensors with that shape, in which case the output is a tensor of densities such that the densities at index i in the output are given by the samples at index i in sample parameterized by the values of mu and sigma at index i.

Examples:

sample = [[-2, -1, 0, 1, 2]]
random_pdf_normal(sample=sample, mu=[0], sigma=[1]) =
    [[0.05399097, 0.24197073, 0.3989423, 0.24197073, 0.05399097]]

random_pdf_normal(sample=sample*2, mu=[0,0], sigma=[1,2]) =
    [[0.05399097, 0.24197073, 0.3989423,  0.24197073, 0.05399097],
     [0.12098537, 0.17603266, 0.19947115, 0.17603266, 0.12098537]]

Defined in /home/smola/mxnet/src/operator/random/pdf_op.cc:L315

Parameters:

  • sample (Symbol) – Samples from the distributions.

  • mu (Symbol) – Means of the distributions.

  • is_log (boolean, optional, default=0) – If set, compute the density of the log-probability instead of the probability.

  • sigma (Symbol) – Standard deviations of the distributions.

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.pdf_poisson(sample=None, lam=None, is_log=_Null, name=None, attr=None, out=None, **kwargs)[source]

Computes the value of the PDF of sample of Poisson distributions with parameters lam (rate).

The shape of lam must match the leftmost subshape of sample. That is, sample can have the same shape as lam, in which case the output contains one density per distribution, or sample can be a tensor of tensors with that shape, in which case the output is a tensor of densities such that the densities at index i in the output are given by the samples at index i in sample parameterized by the value of lam at index i.

Examples:

random_pdf_poisson(sample=[[0,1,2,3]], lam=[1]) =
    [[0.36787945, 0.36787945, 0.18393973, 0.06131324]]

sample = [[0,1,2,3],
          [0,1,2,3],
          [0,1,2,3]]

random_pdf_poisson(sample=sample, lam=[1,2,3]) =
    [[0.36787945, 0.36787945, 0.18393973, 0.06131324],
     [0.13533528, 0.27067056, 0.27067056, 0.18044704],
     [0.04978707, 0.14936121, 0.22404182, 0.22404182]]

Defined in /home/smola/mxnet/src/operator/random/pdf_op.cc:L335

Parameters:

  • sample (Symbol) – Samples from the distributions.

  • lam (Symbol) – Lambda (rate) parameters of the distributions.

  • is_log (boolean, optional, default=0) – If set, compute the density of the log-probability instead of the probability.

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.pdf_uniform(sample=None, low=None, high=None, is_log=_Null, name=None, attr=None, out=None, **kwargs)[source]

Computes the value of the PDF of sample of uniform distributions on the intervals given by [low,high).

low and high must have the same shape, which must match the leftmost subshape of sample. That is, sample can have the same shape as low and high, in which case the output contains one density per distribution, or sample can be a tensor of tensors with that shape, in which case the output is a tensor of densities such that the densities at index i in the output are given by the samples at index i in sample parameterized by the values of low and high at index i.

Examples:

random_pdf_uniform(sample=[[1,2,3,4]], low=[0], high=[10]) = [0.1, 0.1, 0.1, 0.1]

sample = [[[1, 2, 3],
           [1, 2, 3]],
          [[1, 2, 3],
           [1, 2, 3]]]
low  = [[0, 0],
        [0, 0]]
high = [[ 5, 10],
        [15, 20]]
random_pdf_uniform(sample=sample, low=low, high=high) =
    [[[0.2,        0.2,        0.2    ],
      [0.1,        0.1,        0.1    ]],
     [[0.06667,    0.06667,    0.06667],
      [0.05,       0.05,       0.05   ]]]

Defined in /home/smola/mxnet/src/operator/random/pdf_op.cc:L308

Parameters:

  • sample (Symbol) – Samples from the distributions.

  • low (Symbol) – Lower bounds of the distributions.

  • is_log (boolean, optional, default=0) – If set, compute the density of the log-probability instead of the probability.

  • high (Symbol) – Upper bounds of the distributions.

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.poisson(lam=_Null, shape=_Null, ctx=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a Poisson distribution.

Samples are distributed according to a Poisson distribution parametrized by lambda (rate). Samples will always be returned as a floating point data type.

Example:

poisson(lam=4, shape=(2,2)) = [[ 5.,  2.],
                               [ 4.,  6.]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L152

Parameters:

  • lam (float, optional, default=1) – Lambda parameter (rate) of the Poisson distribution.

  • shape (Shape(tuple), optional, default=None) – Shape of the output.

  • ctx (string, optional, default='') – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.

  • dtype ({'None', 'bfloat16', 'float16', 'float32', 'float64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.poisson_like(data=None, lam=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a Poisson distribution according to the input array shape.

Samples are distributed according to a Poisson distribution parametrized by lambda (rate). Samples will always be returned as a floating point data type.

Example:

poisson(lam=4, data=ones(2,2)) = [[ 5.,  2.],
                                  [ 4.,  6.]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L276

Parameters:

  • lam (float, optional, default=1) – Lambda parameter (rate) of the Poisson distribution.

  • data (Symbol) – The input

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.randint(low=_Null, high=_Null, shape=_Null, ctx=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a discrete uniform distribution.

Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high).

Example:

randint(low=0, high=5, shape=(2,2)) = [[ 0,  2],
                                       [ 3,  1]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L210

Parameters:

  • low (long, required) – Lower bound of the distribution.

  • high (long, required) – Upper bound of the distribution.

  • shape (Shape(tuple), optional, default=None) – Shape of the output.

  • ctx (string, optional, default='') – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.

  • dtype ({'None', 'int32', 'int64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to int32 if not defined (dtype=None).

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.uniform(low=_Null, high=_Null, shape=_Null, ctx=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a uniform distribution.

Note

The existing alias uniform is deprecated.

Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high).

Example:

uniform(low=0, high=1, shape=(2,2)) = [[ 0.60276335,  0.85794562],
                                       [ 0.54488319,  0.84725171]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L97

Parameters:

  • low (float, optional, default=0) – Lower bound of the distribution.

  • high (float, optional, default=1) – Upper bound of the distribution.

  • shape (Shape(tuple), optional, default=None) – Shape of the output.

  • ctx (string, optional, default='') – Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.

  • dtype ({'None', 'bfloat16', 'float16', 'float32', 'float64'},optional, default='None') – DType of the output in case this can’t be inferred. Defaults to float32 if not defined (dtype=None).

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol

mxnet.symbol.gen_random.uniform_like(data=None, low=_Null, high=_Null, name=None, attr=None, out=None, **kwargs)[source]

Draw random samples from a uniform distribution according to the input array shape.

Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high).

Example:

uniform(low=0, high=1, data=ones(2,2)) = [[ 0.60276335,  0.85794562],
                                          [ 0.54488319,  0.84725171]]

Defined in /home/smola/mxnet/src/operator/random/sample_op.cc:L226

Parameters:

  • low (float, optional, default=0) – Lower bound of the distribution.

  • high (float, optional, default=1) – Upper bound of the distribution.

  • data (Symbol) – The input

  • name (string, optional.) – Name of the resulting symbol.

Returns:

The result symbol.

Return type:

Symbol