[numpy] Misc fix for other chapters

python/mxnet/_numpy_op_doc.py

@@ -139,3 +139,37 @@ def _npi_multinomial(a):
         In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution.
     """
     pass
+
+
+def _np_cumsum(a, axis=None, dtype=None, out=None):
+    """
+    Return the cumulative sum of the elements along a given axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int, optional
+        Axis along which the cumulative sum is computed. The default
+        (None) is to compute the cumsum over the flattened array.
+    dtype : dtype, optional
+        Type of the returned array and of the accumulator in which the
+        elements are summed.  If `dtype` is not specified, it defaults
+        to the dtype of `a`, unless `a` has an integer dtype with a
+        precision less than that of the default platform integer.  In
+        that case, the default platform integer is used.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output
+        but the type will be cast if necessary. See `doc.ufuncs`
+        (Section "Output arguments") for more details.
+
+    Returns
+    -------
+    cumsum_along_axis : ndarray.
+        A new array holding the result is returned unless `out` is
+        specified, in which case a reference to `out` is returned. The
+        result has the same size as `a`, and the same shape as `a` if
+        `axis` is not None or `a` is a 1-d array.
+    """
+    pass

python/mxnet/gluon/block.py

@@ -35,7 +35,7 @@
 from .utils import _indent, _brief_print_list, HookHandle
 from .utils import _check_same_symbol_type, _check_all_np_ndarrays
 from .. import numpy_extension as _mx_npx
-from .. import numpy as _mx_np
+from .. import numpy as _mx_np, numpy_extension as _mx_npx
 from .. util import is_np_array
 
 
@@ -335,10 +335,8 @@ def save_parameters(self, filename):
         """
         params = self._collect_params_with_prefix()
         arg_dict = {key : val._reduce() for key, val in params.items()}
-        if is_np_array():
-            _mx_np.save(filename, arg_dict)
-        else:
-            ndarray.save(filename, arg_dict)
+        save_fn = _mx_npx.save if is_np_array() else ndarray.save
+        save_fn(filename, arg_dict)
 
     def save_params(self, filename):
         """[Deprecated] Please use save_parameters. Note that if you want load
@@ -382,7 +380,7 @@ def load_parameters(self, filename, ctx=None, allow_missing=False,
         <https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html>`_
         """
         if is_np_array():
-            loaded = _mx_np.load(filename)
+            loaded = _mx_npx.load(filename)
         else:
             loaded = ndarray.load(filename)
         params = self._collect_params_with_prefix()
@@ -911,7 +909,8 @@ def export(self, path, epoch=0, remove_amp_cast=True):
             else:
                 assert name in aux_names
                 arg_dict['aux:%s'%name] = param._reduce()
-        ndarray.save('%s-%04d.params'%(path, epoch), arg_dict)
+        save_fn = _mx_npx.save if is_np_array() else ndarray.save
+        save_fn('%s-%04d.params'%(path, epoch), arg_dict)
 
     def forward(self, x, *args):
         """Defines the forward computation. Arguments can be either

python/mxnet/gluon/data/vision/datasets.py

@@ -83,6 +83,8 @@ def _get_data(self):
         with gzip.open(label_file, 'rb') as fin:
             struct.unpack(">II", fin.read(8))
             label = np.frombuffer(fin.read(), dtype=np.uint8).astype(np.int32)
+            if is_np_array():
+                label = _mx_np.array(label, dtype=label.dtype)
 
         with gzip.open(data_file, 'rb') as fin:
             struct.unpack(">IIII", fin.read(16))

python/mxnet/ndarray/ndarray.py

@@ -2404,7 +2404,7 @@ def _get_broadcast_shape(shape1, shape2):
     for a, b in zip(shape1[::-1], shape2[::-1]):
         if a != 1 and b != 1 and a != b:
             raise ValueError('shape1=%s is not broadcastable to shape2=%s' % (shape1, shape2))
-        shape[i] = max(a, b)
+        shape[i] = b if a == 1 else a
         i -= 1
     return tuple(shape)
 

python/mxnet/ndarray/numpy/_op.py

@@ -27,7 +27,8 @@
 
 __all__ = ['zeros', 'ones', 'maximum', 'minimum', 'stack', 'arange', 'argmax',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'concatenate',
-           'clip', 'split', 'swapaxes', 'expand_dims', 'tile', 'linspace']
+           'clip', 'split', 'swapaxes', 'expand_dims', 'tile', 'linspace',
+           'sin', 'cos', 'sinh', 'cosh', 'log10', 'sqrt']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -99,29 +100,29 @@ def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, ou
 
     Parameters
     --------
-    lhs : NDArray or numeric value
+    lhs : ndarray or numeric value
         Left-hand side operand.
 
-    rhs : NDArray or numeric value
+    rhs : ndarray or numeric value
         Right-hand operand,
 
     fn_array : function
-        Function to be called if both lhs and rhs are of ``NDArray`` type.
+        Function to be called if both lhs and rhs are of ``ndarray`` type.
 
     fn_scalar : function
         Function to be called if both lhs and rhs are numeric values.
 
     lfn_scalar : function
-        Function to be called if lhs is ``NDArray`` while rhs is numeric value
+        Function to be called if lhs is ``ndarray`` while rhs is numeric value
 
     rfn_scalar : function
-        Function to be called if lhs is numeric value while rhs is ``NDArray``;
+        Function to be called if lhs is numeric value while rhs is ``ndarray``;
         if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar
 
     Returns
     --------
-    mxnet.numpy.ndarray
-        result array
+    mxnet.numpy.ndarray or scalar
+        result array or scalar
     """
     from ...numpy import ndarray
     if isinstance(lhs, numeric_types):
@@ -138,7 +139,7 @@ def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, ou
     elif isinstance(rhs, ndarray):
         return fn_array(lhs, rhs, out=out)
     else:
-        raise TypeError('type %s not supported' % str(type(rhs)))
+        raise TypeError('type {} not supported'.format(str(type(rhs))))
 #pylint: enable= too-many-arguments, no-member, protected-access
 
 
@@ -633,7 +634,7 @@ def tile(A, reps):
 
 
 @set_module('mxnet.ndarray.numpy')
-def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, **kwargs): #pylint: disable=too-many-arguments
+def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, **kwargs):  # pylint: disable=too-many-arguments
     """Return evenly spaced numbers over a specified interval.
 
     Returns num evenly spaced samples, calculated over the interval [start, stop].
@@ -653,15 +654,16 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
     endpoint : bool, optional
         If True, stop is the last sample. Otherwise, it is not included.
         Default is True.
-    retstep: bool, optional
+    retstep : bool, optional
         If True, return (samples, step), where step is the spacing between samples.
-    dtype: dtype, optional
+    dtype : dtype, optional
         The type of the output array. If dtype is not given, infer the data
         type from the other input arguments.
     axis : int, optional
         The axis in the result to store the samples. Relevant only if start or
         stop are array-like. By default (0), the samples will be along a new
         axis inserted at the beginning. Use -1 to get an axis at the end.
+
     Returns
     -------
     samples : ndarray
@@ -678,7 +680,7 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
     axis could only be 0 now.
     """
     if isinstance(start, (list, _np.ndarray, NDArray)) or \
-        isinstance(stop, (list, _np.ndarray, NDArray)):
+       isinstance(stop, (list, _np.ndarray, NDArray)):
         raise NotImplementedError('start and stop only support int')
     if axis != 0:
         raise NotImplementedError("the function only support axis 0")
@@ -687,6 +689,196 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
         ctx = current_context()
     if retstep:
         step = (stop - start) / (num - 1)
-        return (_npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype), step)
+        return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype), step
     else:
         return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
+
+
+def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
+    """Helper function for unary operators.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input of the unary operator.
+    fn_array : function
+        Function to be called if x is of ``ndarray`` type.
+    fn_scalar : function
+        Function to be called if x is a Python scalar.
+    out : ndarray
+        The buffer ndarray for storing the result of the unary function.
+
+    Returns
+    -------
+    out : mxnet.numpy.ndarray or scalar
+        Result array or scalar.
+    """
+    if isinstance(x, numeric_types):
+        return fn_scalar(x, **kwargs)
+    elif isinstance(x, NDArray):
+        return fn_array(x, out=out, **kwargs)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
+
+
+@set_module('mxnet.ndarray.numpy')
+def sin(x, out=None, **kwargs):
+    r"""Trigonometric sine, element-wise.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The sine of each element of x. This is a scalar if `x` is a scalar.
+
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.sin, _np.sin, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def cos(x, out=None, **kwargs):
+    r"""Cosine, element-wise.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding cosine values. This is a scalar if x is a scalar.
+
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.cos, _np.cos, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def sinh(x, out=None, **kwargs):
+    """Hyperbolic sine, element-wise.
+
+    Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or ``-1j * np.sin(1j*x)``.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array or scalar.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding hyperbolic sine values. This is a scalar if `x` is a scalar.
+
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.sinh, _np.sinh, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def cosh(x, out=None, **kwargs):
+    """Hyperbolic cosine, element-wise.
+
+    Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``.
+
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array or scalar.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding hyperbolic cosine values. This is a scalar if `x` is a scalar.
+
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.cosh, _np.cosh, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def log10(x, out=None, **kwargs):
+    """Return the base 10 logarithm of the input array, element-wise.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input array or scalar.
+    out : ndarray or None
+        A location into which the result is stored. If provided, it
+        must have a shape that the inputs broadcast to. If not provided
+        or None, a freshly-allocated array is returned. The dtype of the
+        output is the same as that of the input if the input is an ndarray.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The logarithm to the base 10 of `x`, element-wise. NaNs are
+        returned where x is negative. This is a scalar if `x` is a scalar.
+
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.log10, _np.log10, out=out, **kwargs)
+
+
+@set_module('mxnet.ndarray.numpy')
+def sqrt(x, out=None, **kwargs):
+    """
+    Return the non-negative square-root of an array, element-wise.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        The values whose square-roots are required.
+    out : ndarray, or None, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the inputs broadcast to. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        An array of the same shape as `x`, containing the positive
+        square-root of each element in `x`. This is a scalar if `x` is a scalar.
+
+    Notes
+    ----
+    This function only supports input type of float.
+    """
+    return _unary_func_helper(x, _npi.sqrt, _np.sqrt, out=out, **kwargs)