Add symbol api for randn and fix shape issue for randn ndarray and symbol api

mxnet_py3/include/python3.6m

@@ -0,0 +1 @@
+/Users/bapac/anaconda3/include/python3.6m

python/mxnet/ndarray/random.py

@@ -220,8 +220,8 @@ def randn(*shape, **kwargs):
     dtype = kwargs.pop('dtype', _Null)
     ctx = kwargs.pop('ctx', None)
     out = kwargs.pop('out', None)
-    assert isinstance(loc, (int, float))
-    assert isinstance(scale, (int, float))
+    assert isinstance(loc, (int, float, NDArray))
+    assert isinstance(scale, (int, float, NDArray))
     return _random_helper(_internal._random_normal, _internal._sample_normal,
                           [loc, scale], shape, dtype, ctx, out, kwargs)
 

python/mxnet/symbol/random.py

@@ -22,7 +22,7 @@
 from .symbol import Symbol
 
 
-__all__ = ['uniform', 'normal', 'poisson', 'exponential', 'gamma', 'multinomial',
+__all__ = ['uniform', 'normal', 'randn', 'poisson', 'exponential', 'gamma', 'multinomial',
            'negative_binomial', 'generalized_negative_binomial', 'shuffle', 'randint']
 
 
@@ -113,6 +113,36 @@ def normal(loc=0, scale=1, shape=_Null, dtype=_Null, **kwargs):
                           [loc, scale], shape, dtype, kwargs)
 
 
+def randn(*shape, **kwargs):
+    """Draw random samples from a normal (Gaussian) distribution.
+
+    Samples are distributed according to a normal distribution parametrized
+    by *loc* (mean) and *scale* (standard deviation).
+
+
+    Parameters
+    ----------
+    loc : float or Symbol, optional
+        Mean (centre) of the distribution.
+    scale : float or Symbol, optional
+        Standard deviation (spread or width) of the distribution.
+    shape : int or tuple of ints
+        The number of samples to draw. If shape is, e.g., `(m, n)` and `loc` and
+        `scale` are scalars, output shape will be `(m, n)`. If `loc` and `scale`
+        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 `[loc, scale)` pair.
+    dtype : {'float16', 'float32', 'float64'}, optional
+        Data type of output samples. Default is 'float32'
+    """
+    loc = kwargs.pop('loc', 0)
+    scale = kwargs.pop('scale', 1)
+    dtype = kwargs.pop('dtype', _Null)
+    assert isinstance(loc, (int, float, Symbol))
+    assert isinstance(scale, (int, float, Symbol))
+    return _random_helper(_internal._random_normal, _internal._sample_normal,
+                          [loc, scale], shape, dtype, kwargs)
+
+
 def poisson(lam=1, shape=_Null, dtype=_Null, **kwargs):
     """Draw random samples from a Poisson distribution.
 

tests/python/unittest/test_random.py

@@ -61,8 +61,10 @@ def check_with_device(device, dtype):
         },
         {
             'name': 'randn',
+            'symbol': mx.sym.random.randn,
             'ndop': mx.nd.random.randn,
             'params': { 'loc': 10.0, 'scale': 0.5 },
+            'inputs': [ ('loc',[ [ 0.0, 2.5 ], [ -9.75, -7.0 ] ]) , ('scale',[ [ 1.0, 3.7 ], [ 4.2, 1.5 ] ]) ],
             'checks': [
                 ('mean', lambda x, params: np.mean(x.astype(np.float64) - params['loc']),  tol),
                 ('std',  lambda x, params: np.std(x.astype(np.float64)) - params['scale'], tol)
@@ -250,6 +252,9 @@ def check_with_device(device, dtype):
 
         params = {'shape': shape, 'dtype': dtype, 'ctx': device}
         params.update({k : mx.nd.array(v, ctx=device, dtype=dtype) for k, v in symbdic['inputs']})
+        if name == 'randn':
+            params.pop('shape')  # randn does not accept shape param
+            args = shape
         mx.random.seed(128)
         ret1 = ndop(*args, **params).asnumpy()
         mx.random.seed(128)
@@ -263,14 +268,12 @@ def check_with_device(device, dtype):
                     err = np.abs(check_func(ret2[i,j], stats))
                     assert err < tol, "%f vs %f: symbolic test: %s check for `%s` did not pass" % (err, tol, check_name, name)
 
-        if 'symbol' not in symbdic: continue  # randn does not have symbol
-
         # check symbolic
         symbol = symbdic['symbol']
         X = mx.sym.Variable("X")
         params = symbdic['params'].copy()
         params.update(shape=shape, dtype=dtype)
-        if name.endswith('_like'):
+        if name.endswith('_like') or name == 'randn':
             params['data'] = mx.sym.ones(params.pop('shape'))
         Y = symbol(**params) + X
         x = mx.nd.zeros(shape, dtype=dtype, ctx=device)
@@ -298,7 +301,12 @@ def check_with_device(device, dtype):
         single_param = len(symbdic['inputs']) == 1
         v1 = mx.sym.Variable('v1')
         v2 = mx.sym.Variable('v2')
-        Y = symbol(v1,**params) if single_param else symbol(v1,v2,**params)
+        if name == 'randn':
+            params.pop('shape')  # randn does not accept shape param
+            args=shape
+            Y = symbol(v1, **params) if single_param else symbol(*args, loc=v1, scale=v2,**params)
+        else:
+            Y = symbol(v1,**params) if single_param else symbol(v1,v2,**params)
         bindings = { 'v1' : mx.nd.array(symbdic['inputs'][0][1]) }
         if not single_param :
             bindings.update({ 'v2' : mx.nd.array(symbdic['inputs'][1][1]) })
@@ -315,9 +323,10 @@ def check_with_device(device, dtype):
                 for check_name, check_func, tol in symbdic['checks']:
                     assert np.abs(check_func(samples, params)) < tol, "symbolic test: %s check for `%s` did not pass" % (check_name, name)
 
+        if 'pdfsymbol' not in symbdic: continue  # randn not tested for pdf
+
         # check pdfs with only a subset of the generated samples
         un1 = np.resize(un1, (un1.shape[0], un1.shape[1], pdfshape[0], pdfshape[1]))
-        print(name)
         symbol  = symbdic['pdfsymbol']
         pdffunc = symbdic['pdffunc']
         v0 = mx.sym.Variable('v0')
@@ -355,7 +364,6 @@ def check_with_device(device, dtype):
                 check_symbolic_forward(test_pdf, [un1, p1, p2], [res], atol=forw_atol, rtol=forw_rtol, dtype=dtype)
                 if dtype == np.float64:
                   grad_nodes = ['v1', 'v2'] if symbdic['discrete'] else ['v0', 'v1', 'v2']
-                  print(backw_rtol)
                   check_numeric_gradient(test_pdf, [un1, p1, p2], grad_nodes=grad_nodes, atol=backw_atol, rtol=backw_rtol, dtype=dtype)
 
 @with_seed(1000)