[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17360: add random.multivariate_normal, fix empty_like dtype problem, fix gat…

[GitBox <gi...@apache.org>]

haojin2 commented on a change in pull request #17360: add random.multivariate_normal, fix empty_like dtype problem, fix gat…
URL: https://github.com/apache/incubator-mxnet/pull/17360#discussion_r368320815
 
 

 ##########
 File path: python/mxnet/numpy/random.py
 ##########
 @@ -242,6 +242,82 @@ def multinomial(n, pvals, size=None, **kwargs):
     return _mx_nd_np.random.multinomial(n, pvals, size, **kwargs)
 
 
+# pylint: disable=unused-argument
+def multivariate_normal(mean, cov, size=None, check_valid=None, tol=None):
+    """
+    multivariate_normal(mean, cov, size=None, check_valid=None, tol=None)
+
+    Draw random samples from a multivariate normal distribution.
+
+    The multivariate normal, multinormal or Gaussian distribution is a
+    generalization of the one-dimensional normal distribution to higher
+    dimensions.  Such a distribution is specified by its mean and
+    covariance matrix.  These parameters are analogous to the mean
+    (average or "center") and variance (standard deviation, or "width,"
+    squared) of the one-dimensional normal distribution.
+
+    This operator is a little different from the one in official NumPy.
+    The official NumPy operator only accepts 1-D ndarray as mean and 2-D ndarray as cov,
+    whereas the operator in DeepNumPy supports batch operation and auto-broadcasting.
+
+    Both `mean` and `cov` may have any number of leading dimensions, which correspond
+    to a batch shape. They are not necessarily assumed to have the same batch shape,
+    just ones which can be broadcasted.
+
+    Parameters
+    ----------
+    mean : K-D ndarray, of shape (..., N)
+        Mean of the N-dimensional distribution.
+    cov : (K+1)-D ndarray, of shape (..., N, N)
+        Covariance matrix of the distribution. The last two dimensions must be symmetric and
+        positive-semidefinite for proper sampling.
+    size : int or tuple of ints, optional
+        Given a shape of, for example, ``(m,n,k)``,
+        ``m*n*k`` identically distributed batchs of samples are
+        generated, and packed in an `m`-by-`n`-by-`k` arrangement.
+        If no shape is specified, a batch of (`N`-D) sample is returned.
+    check_valid : { 'warn', 'raise', 'ignore' }, optional
+        Behavior when the covariance matrix is not positive semidefinite.
+        (Not supported)
+    tol : float, optional
+        Tolerance when checking the singular values in covariance matrix.
+        cov is cast to double before the check.
+        (Not supported)
+
+    Returns
+    -------
+    out : ndarray
+        The input shape of `mean` and `cov` should satisfy the requirements of broadcasting.
+        If the parameter `size` is not provided,
+        the output shape is ``np.broadcast(mean.shape, cov.shape[:-1])``.
+        Otherwise, the output shape is ``size + np.broadcast(mean.shape, cov.shape[:-1])``
+
+    Examples
+    --------
+    >>> mean = np.array([1, 2])
+    >>> cov = np.array([[1, 0], [0, 1]])
+    >>> x = np.random.multivariate_normal(mean, cov, (3, 3))
+    >>> x.shape
+    (3, 3, 2)
+    # Performs autobroadcasting when the batch shape of
+    # `mean` and `cov` is different but compatible.
+    >>> mean = np.zeros((3,2)) # shape (3, 2)
+    >>> cov = np.array([[1, 0], [0, 100]]) # shape (2, 2)
+    >>> x = np.random.multivariate_normal(mean, cov)
+    >>> x
+    array([[-1.6115597 , -8.726251  ],
+           [ 2.2425299 ,  2.8104177 ],
+           [ 0.36229908, -8.386591  ]])
+
+    The following is probably true, given that 0.6 is roughly twice the
+    standard deviation:
+    >>> list((x[0,0,:] - mean) < 0.6)
+    [True, True] # random
+    """
+    return _mx_nd_np.random.multivariate_normal(mean, cov, size=size,\
 
 Review comment:
   no need for backslash here, and fix alignment

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