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 src/operator/random/sample_op.cc:L137
Lambda parameter (rate) of the exponential distribution.
Shape of the output.
Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L243
Lambda parameter (rate) of the exponential distribution.
The input
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L125
Alpha parameter (shape) of the gamma distribution.
Beta parameter (scale) of the gamma distribution.
Shape of the output.
Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L232
Alpha parameter (shape) of the gamma distribution.
Beta parameter (scale) of the gamma distribution.
The input
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L179
Mean of the negative binomial distribution.
Alpha (dispersion) parameter of the negative binomial distribution.
Shape of the output.
Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L284
Mean of the negative binomial distribution.
Alpha (dispersion) parameter of the negative binomial distribution.
The input
org.apache.mxnet.Symbol
Concurrent sampling from multiple multinomial distributions. *data* is an *n* dimensional array whose last dimension has length *k*, where *k* is the number of possible outcomes of each multinomial distribution. This operator will draw *shape* samples from each distribution. If shape is empty one sample will be drawn from each distribution. If *get_prob* is true, a second array containing log likelihood of the drawn samples will also be returned. This is usually used for reinforcement learning where you can provide reward as head gradient for this array to estimate gradient. Note that the input distribution must be normalized, i.e. *data* must sum to 1 along its last axis. Examples:: probs = `[ [0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0] ] // Draw a single sample for each distribution sample_multinomial(probs) = [3, 0] // Draw a vector containing two samples for each distribution sample_multinomial(probs, shape=(2)) = `[ [4, 2], [0, 0] ] // requests log likelihood sample_multinomial(probs, get_prob=True) = [2, 1], [0.2, 0.3]
Distribution probabilities. Must sum to one on the last axis.
Shape to be sampled from each random distribution.
Whether to also return the log probability of sampled result. This is usually used for differentiating through stochastic variables, e.g. in reinforcement learning.
DType of the output in case this can't be inferred.
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L164
Limit of unsuccessful experiments.
Failure probability in each experiment.
Shape of the output.
Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L268
Limit of unsuccessful experiments.
Failure probability in each experiment.
The input
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L113
Mean of the distribution.
Standard deviation of the distribution.
Shape of the output.
Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L221
Mean of the distribution.
Standard deviation of the distribution.
The input
org.apache.mxnet.Symbol
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 src/operator/random/pdf_op.cc:L316
Samples from the distributions.
Concentration parameters of the distributions.
If set, compute the density of the log-probability instead of the probability.
org.apache.mxnet.Symbol
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 src/operator/random/pdf_op.cc:L305
Samples from the distributions.
Lambda (rate) parameters of the distributions.
If set, compute the density of the log-probability instead of the probability.
org.apache.mxnet.Symbol
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 src/operator/random/pdf_op.cc:L303
Samples from the distributions.
Alpha (shape) parameters of the distributions.
If set, compute the density of the log-probability instead of the probability.
Beta (scale) parameters of the distributions.
org.apache.mxnet.Symbol
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 src/operator/random/pdf_op.cc:L314
Samples from the distributions.
Means of the distributions.
If set, compute the density of the log-probability instead of the probability.
Alpha (dispersion) parameters of the distributions.
org.apache.mxnet.Symbol
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 src/operator/random/pdf_op.cc:L310
Samples from the distributions.
Limits of unsuccessful experiments.
If set, compute the density of the log-probability instead of the probability.
Failure probabilities in each experiment.
org.apache.mxnet.Symbol
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 src/operator/random/pdf_op.cc:L300
Samples from the distributions.
Means of the distributions.
If set, compute the density of the log-probability instead of the probability.
Standard deviations of the distributions.
org.apache.mxnet.Symbol
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 src/operator/random/pdf_op.cc:L307
Samples from the distributions.
Lambda (rate) parameters of the distributions.
If set, compute the density of the log-probability instead of the probability.
org.apache.mxnet.Symbol
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 src/operator/random/pdf_op.cc:L298
Samples from the distributions.
Lower bounds of the distributions.
If set, compute the density of the log-probability instead of the probability.
Upper bounds of the distributions.
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L150
Lambda parameter (rate) of the Poisson distribution.
Shape of the output.
Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L255
Lambda parameter (rate) of the Poisson distribution.
The input
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L194
Lower bound of the distribution.
Upper bound of the distribution.
Shape of the output.
Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
DType of the output in case this can't be inferred. Defaults to int32 if not defined (dtype=None).
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L96
Lower bound of the distribution.
Upper bound of the distribution.
Shape of the output.
Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.
DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).
org.apache.mxnet.Symbol
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 src/operator/random/sample_op.cc:L209
Lower bound of the distribution.
Upper bound of the distribution.
The input
org.apache.mxnet.Symbol
Draw random samples from an an approximately log-uniform or Zipfian distribution without replacement. This operation takes a 2-D shape `(batch_size, num_sampled)`, and randomly generates *num_sampled* samples from the range of integers [0, range_max) for each instance in the batch. The elements in each instance are drawn without replacement from the base distribution. The base distribution for this operator is an approximately log-uniform or Zipfian distribution: P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1) Additionaly, it also returns the number of trials used to obtain `num_sampled` samples for each instance in the batch. Example:: samples, trials = _sample_unique_zipfian(750000, shape=(4, 8192)) unique(samples[0]) = 8192 unique(samples[3]) = 8192 trials[0] = 16435 Defined in src/operator/random/unique_sample_op.cc:L66
The number of possible classes.
2-D shape of the output, where shape[0] is the batch size, and shape[1] is the number of candidates to sample for each batch.
org.apache.mxnet.Symbol