fix coverage prob

python/paddle/fluid/tests/unittests/test_multinomial_op.py

@@ -16,6 +16,7 @@
 
 import unittest
 import paddle
+import paddle.fluid as fluid
 from op_test import OpTest
 import numpy as np
 
@@ -159,6 +160,32 @@ def test_dygraph3(self):
             "replacement is False. categories can't be sampled repeatedly")
         paddle.enable_static()
 
+    def test_static(self):
+        paddle.enable_static()
+        startup_program = fluid.Program()
+        train_program = fluid.Program()
+        with fluid.program_guard(train_program, startup_program):
+            x = fluid.data('x', shape=[4], dtype='float32')
+            out = paddle.multinomial(x, num_samples=100000, replacement=True)
+
+            place = fluid.CPUPlace()
+            if fluid.core.is_compiled_with_cuda():
+                place = fluid.CUDAPlace(0)
+            exe = fluid.Executor(place)
+
+        exe.run(startup_program)
+        x_np = np.random.rand(4).astype('float32')
+        out = exe.run(train_program, feed={'x': x_np}, fetch_list=[out])
+
+        sample_prob = np.unique(out, return_counts=True)[1].astype("float32")
+        sample_prob /= sample_prob.sum()
+
+        prob = x_np / x_np.sum(axis=-1, keepdims=True)
+        self.assertTrue(
+            np.allclose(
+                sample_prob, prob, rtol=0, atol=0.01),
+            "sample_prob: " + str(sample_prob) + "\nprob: " + str(prob))
+
     """
     def test_replacement_error(self):
         def test_error():
@@ -170,5 +197,14 @@ def test_error():
     """
 
 
+class TestMultinomialAlias(unittest.TestCase):
+    def test_alias(self):
+        paddle.disable_static()
+        x = paddle.rand([4])
+        paddle.multinomial(x, num_samples=10, replacement=True)
+        paddle.tensor.multinomial(x, num_samples=10, replacement=True)
+        paddle.tensor.random.multinomial(x, num_samples=10, replacement=True)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/tensor/random.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# TODO: define random functions
+# TODO: define random functions  
 
 from ..fluid import core
 from ..fluid.framework import in_dygraph_mode, Variable, convert_np_dtype_to_dtype_
@@ -40,18 +40,18 @@ def bernoulli(x, name=None):
     This OP returns a Tensor filled with random binary(0 or 1) number from a Bernoulli distribution.
     The input ``x`` is a tensor with probabilities for generating the random binary number.
     Each element in ``x`` should be in [0, 1], and the out is generated by:
-
+    
     .. math::
 
         out_i ~ Bernoulli (x_i)
 
     Args:
-        x(Tensor):  A tensor with probabilities for generating the random binary number. The data type
+        x(Tensor):  A tensor with probabilities for generating the random binary number. The data type 
             should be float32, float64.
         name(str, optional): The default value is None. Normally there is no
             need for user to set this property. For more information, please
             refer to :ref:`api_guide_Name`.
-    Returns:
+    Returns: 
         Tensor: A Tensor filled with random binary number with the same shape and dtype as ``x``.
 
     Examples:
@@ -80,7 +80,7 @@ def bernoulli(x, name=None):
 
     helper = LayerHelper("randint", **locals())
     out = helper.create_variable_for_type_inference(
-        dtype=x.dtype)  # maybe set out to int32 ?
+        dtype=x.dtype)  # maybe set out to int32 ? 
     helper.append_op(
         type='bernoulli', inputs={"X": x}, outputs={'Out': out}, attrs={})
     return out
@@ -122,7 +122,7 @@ def multinomial(x, num_samples=1, replacement=False, name=None):
         # [[3 3 1 1 0]
         # [0 0 0 0 1]]
 
-        out2 = paddle.multinomial(x, num_samples=5)
+        # out2 = paddle.multinomial(x, num_samples=5)
         # OutOfRangeError: When replacement is False, number of samples
         #  should be less than non-zero categories
 
@@ -176,7 +176,7 @@ def gaussian(shape, mean=0.0, std=1.0, dtype=None, name=None):
 
     Returns:
         Tensor: A Tensor filled with random values sampled from a Gaussian
-        distribution, with ``shape`` and ``dtype``.
+        distribution, with ``shape`` and ``dtype``. 
     """
     op_type_for_check = 'gaussian/standard_normal/randn/normal'
     seed = 0
@@ -417,7 +417,7 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
 
     Examples:
         .. code-block:: python
-
+            
             import paddle
 
             paddle.disable_static()
@@ -505,7 +505,7 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
             need for user to set this property.  For more information, please
             refer to :ref:`api_guide_Name`.
 
-    Returns:
+    Returns: 
         Tensor: A Tensor filled with random integers from a discrete uniform
         distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``.
 
@@ -615,7 +615,7 @@ def randperm(n, dtype="int64", name=None):
 
             out2 = paddle.randperm(7, 'int32')
             # [1, 6, 2, 0, 4, 3, 5]  # random
-
+ 
     """
     if not isinstance(dtype, core.VarDesc.VarType):
         dtype = convert_np_dtype_to_dtype_(dtype)