modify docs

python/mxnet/ndarray/numpy/_op.py

@@ -94,7 +94,9 @@ def zeros(shape, dtype=float, order='C', ctx=None):  # pylint: disable=redefined
     shape : int or tuple of int
         The shape of the empty array.
     dtype : str or numpy.dtype, optional
-        An optional value type (default is `float32` or 'float64', which depends on your current default dtype).
+        An optional value type.
+        - When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        - When npx.is_np_default_dtype() returns True, default dtype is float64.
         Note that this behavior is different from NumPy's `zeros` function where `float64`
         is the default value, here we can set 'float32' or 'float64' as your default dtype,
         because `float32` is considered as the default data type in deep learning.
@@ -129,7 +131,9 @@ def ones(shape, dtype=None, order='C', ctx=None):  # pylint: disable=redefined-o
     shape : int or tuple of int
         The shape of the empty array.
     dtype : str or numpy.dtype, optional
-        An optional value type. Default is depend on your current default dtype.
+        An optional value type.
+        - When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        - When npx.is_np_default_dtype() returns True, default dtype is float64.
         Note that this behavior is different from NumPy's `ones` function where
         `float64` is the default value.
     order : {'C'}, optional, default: 'C'
@@ -315,6 +319,8 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
     dtype : data-type, optional
         The desired data-type for the array. The default, `None`, means
         `np.array(fill_value).dtype`.
+        - When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        - When npx.is_np_default_dtype() returns True, default dtype is float64.
     order : {'C'}, optional
         Whether to store multidimensional data in C- or Fortran-contiguous
         (row- or column-wise) order in memory. Currently only supports C order.
@@ -524,8 +530,9 @@ def arange(start, stop=None, step=1, dtype=None, ctx=None):
         step size is 1.  If `step` is specified as a position argument,
         `start` must also be given.
     dtype : dtype
-        The type of the output array. The default is `float32` or 'float64',
-        which depends on your current default dtype.
+        The type of the output array.
+        - When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        - When npx.is_np_default_dtype() returns True, default dtype is float64.
 
     Returns
     -------
@@ -565,7 +572,8 @@ def identity(n, dtype=None, ctx=None):
         Number of rows (and columns) in `n` x `n` output.
     dtype : data-type, optional
         Data-type of the output.
-        Default dtype is 'float32' or 'float64', which depends on your current default dtype.
+        - When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        - When npx.is_np_default_dtype() returns True, default dtype is float64.
     ctx : Context, optional
         An optional device context (default is the current default context).
 
@@ -1077,8 +1085,9 @@ def divide(x1, x2, out=None, **kwargs):
     according to the following rules:
         * If both inputs are of floating number types, the output is the more precise type.
         * If only one of the inputs is floating number type, the result is that type.
-        * If both inputs are of integer types (including boolean), the output is of float32 or
-          float64 type, which depends on your current default dtype.
+        * If both inputs are of integer types (including boolean), the output is of default dtype.
+          - When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+          - When npx.is_np_default_dtype() returns True, default dtype is float64.
     """
     return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
                          _npi.rtrue_divide_scalar, out)
@@ -1116,8 +1125,9 @@ def true_divide(x1, x2, out=None):
     according to the following rules:
         * If both inputs are of floating number types, the output is the more precise type.
         * If only one of the inputs is floating number type, the result is that type.
-        * If both inputs are of integer types (including boolean), the output is of float32 or
-          float64 type, which depends on your current default dtype.
+        * If both inputs are of integer types (including boolean), the output is of default dtype.
+          - When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+          - When npx.is_np_default_dtype() returns True, default dtype is float64.
     """
     return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar,
                          _npi.rtrue_divide_scalar, out)
@@ -1621,6 +1631,8 @@ def eye(N, M=None, k=0, dtype=float, **kwargs):
         and a negative value to a lower diagonal.
     dtype : data-type, optional
         Data-type of the returned array.
+        - When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        - When npx.is_np_default_dtype() returns True, default dtype is float64.
 
     Returns
     -------
@@ -4505,8 +4517,9 @@ def average(a, axis=None, weights=None, returned=False, out=None):
         Return the average along the specified axis.
         When returned is True, return a tuple with the average as the first element
         and the sum of the weights as the second element. sum_of_weights is of the same type as retval.
-        If a is integral, the result dtype will be float32 or float64, which depends on your current
-        default dtype, otherwise it will be the same as dtype of a.
+        If a is integral, the result dtype will be current default dtype, otherwise it will be the same
+        as dtype of a. (i.e. When npx.is_np_default_dtype() returns Flase, default dtype is float32; When
+        npx.is_np_default_dtype() returns True, default dtype is float64.)
 
     Raises
     --------
@@ -4529,7 +4542,9 @@ def average(a, axis=None, weights=None, returned=False, out=None):
     - Does not guarantee the same behavior with numpy when given float16 dtype and overflow happens
     - Does not support complex dtype
     - The dtypes of a and weights must be the same
-    - Integral a results in float32 or float64 returned dtype, which depends on your current default dtype
+    - Integral a results in default dtype.
+      i.e. When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+      When npx.is_np_default_dtype() returns True, default dtype is float64.
 
     Examples
     --------
@@ -4573,8 +4588,9 @@ def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable
         instead of a single axis or all the axes as before.
     dtype : data-type, optional
         Type to use in computing the mean.
-        For integer inputs, the default is float32 or float64, which depends on your current default dtype ;
-        for floating point inputs, it is the same as the input dtype.
+        For integer inputs, the default is your current default dtype (i.e. When npx.is_np_default_dtype() returns
+        Flase, default dtype is float32; When npx.is_np_default_dtype() returns True, default dtype is float64.);
+        For floating point inputs, it is the same as the input dtype.
     out : ndarray, optional
         Alternate output array in which to place the result. The default is None; if provided,
         it must have the same shape and type as the expected output
@@ -4596,7 +4612,9 @@ def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable
     <https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html>`_ in
     the following way(s):
     - only ndarray is accepted as valid input, python iterables or scalar is not supported
-    - default data type for integer input is float32 or float64, which depends on your current default dtype
+    - default data type for integer input is float32 or float64, which depends on your current default dtype.
+      When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+      When npx.is_np_default_dtype() returns True, default dtype is float64.
     Examples
     --------
     >>> a = np.array([[1, 2], [3, 4]])
@@ -4700,9 +4718,11 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
         If this is a tuple of ints, a variance is performed over multiple axes,
         instead of a single axis or all the axes as before.
     dtype : data-type, optional
-        Type to use in computing the variance. For arrays of integer type
-        the default is `float32` or 'float64', which depends on your current
-        default dtype; for arrays of float types it is the same as the array type.
+        Type to use in computing the variance.
+        For arrays of integer type the default is `float32` or 'float64',
+        When npx.is_np_default_dtype() returns Flase, default dtype is float32,
+        When npx.is_np_default_dtype() returns True, default dtype is float64;
+        For arrays of float types it is the same as the array type.
     out : ndarray, optional
         Alternate output array in which to place the result.  It must have
         the same shape as the expected output, but the type is cast if
@@ -4763,7 +4783,7 @@ def indices(dimensions, dtype=None, ctx=None):
     dimensions : sequence of ints
         The shape of the grid.
     dtype : data-type, optional
-        The desired data-type for the array. Default is `int32`.
+        The desired data-type for the array. Default is `int64`.
     ctx : device context, optional
         Device context on which the memory is allocated. Default is
         `mxnet.context.current_context()`.
@@ -4793,10 +4813,10 @@ def indices(dimensions, dtype=None, ctx=None):
     (2, 2, 3)
     >>> grid[0]        # row indices
     array([[0, 0, 0],
-           [1, 1, 1]], dtype=int32)
+           [1, 1, 1]], dtype=int64)
     >>> grid[1]        # column indices
     array([[0, 0, 0],
-           [1, 1, 1]], dtype=int32)
+           [1, 1, 1]], dtype=int64)
 
     The indices can be used as an index into an array.
 
@@ -5018,7 +5038,8 @@ def hanning(M, ctx=None):
     out : ndarray, shape(M,)
         The window, with the maximum value normalized to one (the value
         one appears only if `M` is odd).
-        Default is 'float32' or 'float64', which depends on your current default dtype.
+        When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        When npx.is_np_default_dtype() returns True, default dtype is float64.
         Note that you need select numpy.float32 or float64 in this operator.
 
     See Also
@@ -5099,7 +5120,8 @@ def hamming(M, ctx=None):
     out : ndarray, shape(M,)
         The window, with the maximum value normalized to one (the value
         one appears only if `M` is odd).
-        Default is 'float32' or 'float64', which depends on your current default dtype.
+        When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        When npx.is_np_default_dtype() returns True, default dtype is float64.
         Note that you need select numpy.float32 or float64 in this operator.
 
     See Also
@@ -5181,7 +5203,8 @@ def blackman(M, ctx=None):
     out : ndarray
         The window, with the maximum value normalized to one (the value one
         appears only if the number of samples is odd).
-        Default is 'float32' or 'float64', which depends on your current default dtype.
+        When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        When npx.is_np_default_dtype() returns True, default dtype is float64.
         Note that you need select numpy.float32 or float64 in this operator.
 
     See Also

python/mxnet/ndarray/numpy/random.py

@@ -111,7 +111,9 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
         a scalar tensor containing a single value is returned if
         ``low`` and ``high`` are both scalars.
     dtype : {'float16', 'float32', 'float64'}, optional
-        Data type of output samples. Default is 'float32'
+        Data type of output samples.
+        When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        When npx.is_np_default_dtype() returns True, default dtype is float64.
     ctx : Context, optional
         Device context of output. Default is current context.
     out : ``ndarray``, optional
@@ -160,7 +162,9 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
         samples are drawn. If size is `None` (default), a scalar tensor containing
         a single value is returned if loc and scale are both scalars.
     dtype : {'float16', 'float32', 'float64'}, optional
-        Data type of output samples. Default is 'float32'
+        Data type of output samples.
+        When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        When npx.is_np_default_dtype() returns True, default dtype is float64.
     ctx : Context, optional
         Device context of output. Default is current context.
     out : ``ndarray``, optional
@@ -768,7 +772,9 @@ def gamma(shape, scale=1.0, size=None, dtype=None, ctx=None, out=None):
         a single value is returned if ``shape`` and ``scale`` are both scalars.
         Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn.
     dtype : {'float16', 'float32', 'float64'}, optional
-        Data type of output samples. Default is 'float32'.
+        Data type of output samples.
+        When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        When npx.is_np_default_dtype() returns True, default dtype is float64.
     ctx : Context, optional
         Device context of output. Default is current context.
 
@@ -834,7 +840,9 @@ def beta(a, b, size=None, dtype=None, ctx=None):
         a single value is returned if ``a`` and ``b`` are both scalars.
         Otherwise, ``np.broadcast(a, b).size`` samples are drawn.
     dtype : {'float16', 'float32', 'float64'}, optional
-        Data type of output samples. Default is 'float32'.
+        Data type of output samples.
+        When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        When npx.is_np_default_dtype() returns True, default dtype is float64.
     ctx : Context, optional
         Device context of output. Default is current context.
 
@@ -881,7 +889,9 @@ def chisquare(df, size=None, dtype=None, ctx=None):
         a single value is returned if ``df`` is a scalar.  Otherwise,
         ``np.array(df).size`` samples are drawn.
     dtype : {'float16', 'float32', 'float64'}, optional
-        Data type of output samples. Default is 'float32'.
+        Data type of output samples.
+        When npx.is_np_default_dtype() returns Flase, default dtype is float32;
+        When npx.is_np_default_dtype() returns True, default dtype is float64.
         Dtype 'float32' or 'float64' is strongly recommended,
         since lower precision might lead to out of range issue.
     ctx : Context, optional