[FEATURE] Add binomial sampling and fix multinomial sampling

python/mxnet/amp/lists/symbol_fp16.py

@@ -336,6 +336,8 @@
     '_random_exponential_like',
     '_random_gamma',
     '_random_gamma_like',
+    '_random_binomial',
+    '_random_binomial_like',
     '_random_generalized_negative_binomial',
     '_random_generalized_negative_binomial_like',
     '_random_negative_binomial',
@@ -353,7 +355,9 @@
     '_rnn_param_concat',
     '_sample_exponential',
     '_sample_gamma',
+    '_sample_binomial',
     '_sample_generalized_negative_binomial',
+    '_sample_categorical',
     '_sample_multinomial',
     '_sample_negative_binomial',
     '_sample_normal',

python/mxnet/ndarray/random.py

@@ -23,8 +23,8 @@
 from .ndarray import NDArray
 
 
-__all__ = ['uniform', 'normal', 'randn', 'poisson', 'exponential', 'gamma',
-           'multinomial', 'negative_binomial', 'generalized_negative_binomial',
+__all__ = ['uniform', 'normal', 'randn', 'poisson', 'exponential', 'gamma', 'binomial',
+           'categorical', 'multinomial', 'negative_binomial', 'generalized_negative_binomial',
            'shuffle', 'randint']
 
 
@@ -383,6 +383,59 @@ def gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwarg
                           [alpha, beta], shape, dtype, ctx, out, kwargs)
 
 
+def binomial(n=1, p=0.5, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
+    """Draw random samples from a binomial distribution.
+
+    Samples are distributed according to a binomial distribution parametrized
+    by *n* (number of trials) and *p* (success probability).
+
+    Parameters
+    ----------
+    n : float or NDArray, optional
+        Number of experiments, > 0.
+    p : float or NDArray, optional
+        Success probability in each experiment, >= 0 and <= 1.
+    shape : int or tuple of ints, optional
+        The number of samples to draw. If shape is, e.g., `(m, n)` and `n` and
+        `p` are scalars, output shape will be `(m, n)`. If `n` and `p`
+        are NDArrays with shape, e.g., `(x, y)`, then output will have shape
+        `(x, y, m, n)`, where `m*n` samples are drawn for each `[n, p)` pair.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    ctx : Context, optional
+        Device context of output. Default is current context. Overridden by
+        `n.context` when `n` is an NDArray.
+    out : NDArray, optional
+        Store output to an existing NDArray.
+
+    Returns
+    -------
+    NDArray
+        If input `shape` has shape, e.g., `(m, n)` and `n` and `p` are scalars, output
+        shape will be `(m, n)`. If `n` and `p` are NDArrays with shape, e.g.,
+        `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are
+        drawn for each `[n, p)` pair.
+
+    Examples
+    --------
+    >>> mx.nd.random.binomial(10, 0.1)
+    [ 1.]
+    <NDArray 1 @cpu(0)>
+    >>> mx.nd.random.binomial(10, 0.6, shape=(2,))
+    [ 4. 6.]
+    <NDArray 2 @cpu(0)>
+    >>> n = mx.nd.array([10,2,3])
+    >>> p = mx.nd.array([0.2,0.3,0.4])
+    >>> mx.nd.random.binomial(n, p, shape=2)
+    [[  1. 4.]
+     [  0. 2.]
+     [  1. 1.]]
+    <NDArray 3x2 @cpu(0)>
+    """
+    return _random_helper(_internal._random_binomial, _internal._sample_binomial,
+                          [n, p], shape, dtype, ctx, out, kwargs)
+
+
 def negative_binomial(k=1, p=1, shape=_Null, dtype=_Null, ctx=None,
                       out=None, **kwargs):
     """Draw random samples from a negative binomial distribution.
@@ -496,9 +549,8 @@ def generalized_negative_binomial(mu=1, alpha=1, shape=_Null, dtype=_Null, ctx=N
                           _internal._sample_generalized_negative_binomial,
                           [mu, alpha], shape, dtype, ctx, out, kwargs)
 
-
-def multinomial(data, shape=_Null, get_prob=False, out=None, dtype='int32', **kwargs):
-    """Concurrent sampling from multiple multinomial distributions.
+def categorical(data, shape=_Null, get_prob=False, out=None, dtype='int32', **kwargs):
+    """Concurrent sampling from multiple categorical distributions.
 
     .. note:: The input distribution must be normalized, i.e. `data` must sum to
               1 along its last dimension.
@@ -507,8 +559,8 @@ def multinomial(data, shape=_Null, get_prob=False, out=None, dtype='int32', **kw
     ----------
     data : NDArray
         An *n* dimensional array whose last dimension has length `k`, where
-        `k` is the number of possible outcomes of each multinomial distribution.
-        For example, data with shape `(m, n, k)` specifies `m*n` multinomial
+        `k` is the number of possible outcomes of each categorical distribution.
+        For example, data with shape `(m, n, k)` specifies `m*n` categorical
         distributions each with `k` possible outcomes.
     shape : int or tuple of ints, optional
         The number of samples to draw from each distribution. If shape is empty
@@ -530,7 +582,7 @@ def multinomial(data, shape=_Null, get_prob=False, out=None, dtype='int32', **kw
         For input `data` with `n` dimensions and shape `(d1, d2, ..., dn-1, k)`, and input
         `shape` with shape `(s1, s2, ..., sx)`, returns an NDArray with shape
         `(d1, d2, ... dn-1, s1, s2, ..., sx)`. The `s1, s2, ... sx` dimensions of the
-        returned NDArray consist of 0-indexed values sampled from each respective multinomial
+        returned NDArray consist of 0-indexed values sampled from each respective categorical
         distribution provided in the `k` dimension of `data`.
 
         For the case `n`=1, and `x`=1 (one shape dimension), returned NDArray has shape `(s1,)`.
@@ -542,24 +594,80 @@ def multinomial(data, shape=_Null, get_prob=False, out=None, dtype='int32', **kw
     Examples
     --------
     >>> probs = mx.nd.array([0, 0.1, 0.2, 0.3, 0.4])
-    >>> mx.nd.random.multinomial(probs)
+    >>> mx.nd.random.categorical(probs)
     [3]
     <NDArray 1 @cpu(0)>
     >>> probs = mx.nd.array([[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]])
-    >>> mx.nd.random.multinomial(probs)
+    >>> mx.nd.random.categorical(probs)
     [3 1]
     <NDArray 2 @cpu(0)>
-    >>> mx.nd.random.multinomial(probs, shape=2)
+    >>> mx.nd.random.categorical(probs, shape=2)
     [[4 4]
      [1 2]]
     <NDArray 2x2 @cpu(0)>
-    >>> mx.nd.random.multinomial(probs, get_prob=True)
+    >>> mx.nd.random.categorical(probs, get_prob=True)
     [3 2]
     <NDArray 2 @cpu(0)>
     [-1.20397282 -1.60943794]
     <NDArray 2 @cpu(0)>
     """
-    return _internal._sample_multinomial(data, shape, get_prob, out=out, dtype=dtype, **kwargs)
+    return _internal._sample_categorical(data, shape, get_prob, out=out, dtype=dtype, **kwargs)
+
+
+def multinomial(n=[1], p=[[1.0]], shape=_Null, dtype='float32', ctx=None, out=None, **kwargs):
+    """Concurrent sampling from multiple multinomial distributions.
+
+    .. note:: The input distribution must be normalized, i.e. `p` must sum to
+              1 along its last dimension.
+
+    Parameters
+    ----------
+    n : NDArray
+        An *n* dimensional array containing the number of trials of each
+        multinomial distribution.
+    p : NDArray
+        An *n+1* dimensional array containing the probabilities of each multinomial
+        distribution. Its last dimension has length `k`, where `k` is the number
+        of possible outcomes of each multinomial distribution.
+        For example, p with shape `(m, n, k)` specifies `m*n` multinomial
+        distributions each with `k` possible outcomes.
+    shape : int or tuple of ints, optional
+        The number of samples to draw from each distribution. If shape is empty
+        one sample will be drawn from each distribution.
+    out : NDArray, optional
+        Store output to an existing NDArray.
+    ctx : Context, optional
+        Device context of output. Default is current context. Overridden by
+        `n.context` when `n` is an NDArray.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+
+    Returns
+    -------
+    NDArray
+        If input `shape` has shape, e.g., `(m, n)` and `n` and `p` are a scalar and an array of length k
+        respectively, output shape will be `(m, n, k)`. If `n` and `p` are NDArrays with shape, e.g.,
+        `(x, y)` and `(x, y, k)`, then output will have shape `(x, y, m, n, k)`, where `m*n`
+        samples are drawn for each `[n, p)` pair.
+
+    Examples
+    --------
+    >>> mx.nd.random.multinomial(mx.nd.array([10]), mx.nd.array([[0.1, 0.9]]))
+    [[ 1. 9.]]
+    <NDArray 1x2 @cpu(0)>
+    >>> mx.nd.random.multinomial(mx.nd.array([10]), mx.nd.array([[0.6, 0.4]]), shape=(2,))
+    [[[ 5. 5.]
+      [ 6. 4.]]]
+    <NDArray 1x2x2 @cpu(0)>
+    >>> n = mx.nd.array([10, 2, 3])
+    >>> p = mx.nd.array([[0.2, 0.8], [0.3, 0.7], [0.4, 0.6]])
+    >>> mx.nd.random.binomial(n, p)
+    [[  2. 8.]
+     [  1. 1.]
+     [  1. 2.]]
+    <NDArray 3x2 @cpu(0)>
+    """
+    return _internal._sample_multinomial(n, p, shape=shape, out=out, ctx=ctx, dtype=dtype, **kwargs)
 
 
 def shuffle(data, **kwargs):

python/mxnet/symbol/random.py

@@ -22,8 +22,8 @@
 from .symbol import Symbol
 
 
-__all__ = ['uniform', 'normal', 'randn', 'poisson', 'exponential', 'gamma', 'multinomial',
-           'negative_binomial', 'generalized_negative_binomial', 'shuffle', 'randint']
+__all__ = ['uniform', 'normal', 'randn', 'poisson', 'exponential', 'gamma', 'categorical', 'multinomial',
+           'binomial', 'negative_binomial', 'generalized_negative_binomial', 'shuffle', 'randint']
 
 
 def _random_helper(random, sampler, params, shape, dtype, kwargs):
@@ -240,6 +240,38 @@ def gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, **kwargs):
                           [alpha, beta], shape, dtype, kwargs)
 
 
+def binomial(n=1, p=0.5, shape=_Null, dtype=_Null, **kwargs):
+    """Draw random samples from a binomial distribution.
+
+    Samples are distributed according to a binomial distribution parametrized
+    by *n* (number of trials) and *p* (success probability).
+
+    Parameters
+    ----------
+    n : float or Symbol, optional
+        Number of experiments, > 0.
+    p : float or Symbol, optional
+        Success probability in each experiment, >= 0 and <= 1.
+    shape : int or tuple of ints, optional
+        The number of samples to draw. If shape is, e.g., `(m, n)` and `n` and
+        `p` are scalars, output shape will be `(m, n)`. If `n` and `p`
+        are NDArrays with shape, e.g., `(x, y)`, then output will have shape
+        `(x, y, m, n)`, where `m*n` samples are drawn for each `[n, p)` pair.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+
+    Returns
+    -------
+    Symbol
+        If input `shape` has shape, e.g., `(m, n)` and `n` and `p` are scalars, output
+        shape will be `(m, n)`. If `n` and `p` are NDArrays with shape, e.g.,
+        `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are
+        drawn for each `[n, p)` pair.
+    """
+    return _random_helper(_internal._random_binomial, _internal._sample_binomial,
+                          [n, p], shape, dtype, kwargs)
+
+
 def negative_binomial(k=1, p=1, shape=_Null, dtype=_Null, **kwargs):
     """Draw random samples from a negative binomial distribution.
 
@@ -311,8 +343,8 @@ def generalized_negative_binomial(mu=1, alpha=1, shape=_Null, dtype=_Null, **kwa
                           [mu, alpha], shape, dtype, kwargs)
 
 
-def multinomial(data, shape=_Null, get_prob=True, dtype='int32', **kwargs):
-    """Concurrent sampling from multiple multinomial distributions.
+def categorical(data, shape=_Null, get_prob=True, dtype='int32', **kwargs):
+    """Concurrent sampling from multiple categorical distributions.
 
     .. note:: The input distribution must be normalized, i.e. `data` must sum to
               1 along its last dimension.
@@ -321,8 +353,8 @@ def multinomial(data, shape=_Null, get_prob=True, dtype='int32', **kwargs):
     ----------
     data : Symbol
         An *n* dimensional array whose last dimension has length `k`, where
-        `k` is the number of possible outcomes of each multinomial distribution.
-        For example, data with shape `(m, n, k)` specifies `m*n` multinomial
+        `k` is the number of possible outcomes of each categorical distribution.
+        For example, data with shape `(m, n, k)` specifies `m*n` categorical
         distributions each with `k` possible outcomes.
     shape : int or tuple of ints, optional
         The number of samples to draw from each distribution. If shape is empty
@@ -343,7 +375,7 @@ def multinomial(data, shape=_Null, get_prob=True, dtype='int32', **kwargs):
         `shape` with shape `(s1, s2, ..., sx)`, returns a Symbol that resovles to shape
         `(d1, d2, ... dn-1, s1, s2, ..., sx)`. The `s1, s2, ... sx` dimensions of the
         returned Symbol's resolved value will consist of 0-indexed values sampled from each
-        respective multinomial distribution provided in the `k` dimension of `data`.
+        respective categorical distribution provided in the `k` dimension of `data`.
 
         For the case `n`=1, and `x`=1 (one shape dimension), returned Symbol will resolve to
         shape `(s1,)`.
@@ -352,7 +384,41 @@ def multinomial(data, shape=_Null, get_prob=True, dtype='int32', **kwargs):
         outputs: `[ndarray_output, log_likelihood_output]`, where `log_likelihood_output` will resolve
         to the same shape as the sampled outputs in ndarray_output.
     """
-    return _internal._sample_multinomial(data, shape, get_prob, dtype=dtype, **kwargs)
+    return _internal._sample_categorical(data, shape, get_prob, dtype=dtype, **kwargs)
+
+
+def multinomial(n=[1], p=[[1.0]], shape=_Null, dtype='float32', **kwargs):
+    """Concurrent sampling from multiple multinomial distributions.
+
+    .. note:: The input distribution must be normalized, i.e. `p` must sum to
+              1 along its last dimension.
+
+    Parameters
+    ----------
+    n : Symbol
+        An *n* dimensional array containing the number of trials of each
+        multinomial distribution.
+    p : Symbol
+        An *n+1* dimensional array containing the probabilities of each multinomial
+        distribution. Its last dimension has length `k`, where `k` is the number
+        of possible outcomes of each multinomial distribution.
+        For example, p with shape `(m, n, k)` specifies `m*n` multinomial
+        distributions each with `k` possible outcomes.
+    shape : int or tuple of ints, optional
+        The number of samples to draw from each distribution. If shape is empty
+        one sample will be drawn from each distribution.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+
+    Returns
+    -------
+    Symbol
+        If input `shape` has shape, e.g., `(m, n)` and `n` and `p` are a scalar and an array of length k
+        respectively, output shape will be `(m, n, k)`. If `n` and `p` are NDArrays with shape, e.g.,
+        `(x, y)` and `(x, y, k)`, then output will have shape `(x, y, m, n, k)`, where `m*n`
+        samples are drawn for each `[n, p)` pair.
+    """
+    return _internal._sample_multinomial(n, p, shape, dtype=dtype, **kwargs)
 
 
 def shuffle(data, **kwargs):

src/operator/random/multisample_op.cc

@@ -218,6 +218,37 @@ Examples::
 )code");
 }
 
+inline std::string binomial_desc() {
+  return std::string(R"code(Concurrent sampling from multiple
+binomial distributions with parameters *n* (number of trials) and *p* (success probability).
+
+The parameters of the distributions are provided as input arrays.
+Let *[s]* be the shape of the input arrays, *n* be the dimension of *[s]*, *[t]*
+be the shape specified as the parameter of the operator, and *m* be the dimension
+of *[t]*. Then the output will be a *(n+m)*-dimensional array with shape *[s]x[t]*.
+
+For any valid *n*-dimensional index *i* with respect to the input arrays, *output[i]*
+will be an *m*-dimensional array that holds randomly drawn samples from the distribution
+which is parameterized by the input values at index *i*. If the shape parameter of the
+operator is not set, then one sample will be drawn per distribution and the output array
+has the same shape as the input arrays.
+
+Samples will always be returned as a floating point data type.
+
+Examples::
+
+   n = [ 20, 49 ]
+   p = [ 0.4 , 0.77 ]
+
+   // Draw a single sample for each distribution
+   sample_binomial(n, p) = [ 5.,  36.]
+
+   // Draw a vector containing two samples for each distribution
+   sample_binomial(n, p, shape=(2)) = [[ 5.,  40.],
+                                       [ 11.,  35.]]
+)code");
+}
+
 inline std::string negative_binomial_desc() {
   return std::string(R"code(Concurrent sampling from multiple
 negative binomial distributions with parameters *k* (failure limit) and *p* (failure probability).
@@ -312,6 +343,13 @@ MXNET_OPERATOR_REGISTER_SAMPLING1(poisson,
                                   "Lambda (rate) parameters of the distributions.",
                                   poisson_desc)
     .add_alias("_npx_tensor_poisson");
+MXNET_OPERATOR_REGISTER_SAMPLING2(binomial,
+                                  BinomialSampler<cpu>,
+                                  "n",
+                                  "p",
+                                  "Number of experiments.",
+                                  "Success probabilities in each experiment.",
+                                  binomial_desc);
 MXNET_OPERATOR_REGISTER_SAMPLING2(negative_binomial,
                                   NegativeBinomialSampler<cpu>,
                                   "k",

src/operator/random/multisample_op.cu

@@ -42,6 +42,9 @@ NNVM_REGISTER_OP(_sample_exponential)
 NNVM_REGISTER_OP(_sample_poisson)
     .set_attr<FCompute>("FCompute<gpu>", MultiSampleOpForward<gpu, PoissonSampler<gpu>, 1>);
 
+NNVM_REGISTER_OP(_sample_binomial)
+    .set_attr<FCompute>("FCompute<gpu>", MultiSampleOpForward<gpu, BinomialSampler<gpu>, 2>);
+
 NNVM_REGISTER_OP(_sample_negative_binomial)
     .set_attr<FCompute>("FCompute<gpu>",
                         MultiSampleOpForward<gpu, NegativeBinomialSampler<gpu>, 2>);