Numpy compatible linspace

python/mxnet/ndarray/numpy/_op.py

@@ -23,10 +23,11 @@
 from ...util import _sanity_check_params, set_module
 from ...context import current_context
 from . import _internal as _npi
+from ..ndarray import NDArray
 
 __all__ = ['zeros', 'ones', 'maximum', 'minimum', 'stack', 'arange', 'argmax',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'concatenate',
-           'clip', 'split', 'swapaxes', 'expand_dims', 'tile']
+           'clip', 'split', 'swapaxes', 'expand_dims', 'tile', 'linspace']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -629,3 +630,63 @@ def tile(A, reps):
         The tiled output array.
     """
     return _npi.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
+    """Return evenly spaced numbers over a specified interval.
+
+    Returns num evenly spaced samples, calculated over the interval [start, stop].
+    The endpoint of the interval can optionally be excluded.
+
+    Parameters
+    ----------
+    start : array_like
+        The starting value of the sequence.
+    stop : array_like
+        The end value of the sequence, unless endpoint is set to False. In
+        that case, the sequence consists of all but the last of num + 1
+        evenly spaced samples, so that stop is excluded. Note that the step
+        size changes when endpoint is False.
+    num : int, optional
+        Number of samples to generate. Default is 50. Must be non-negative.
+    endpoint : bool, optional
+        If True, stop is the last sample. Otherwise, it is not included.
+        Default is True.
+    retstep: bool, optional
+        If True, return (samples, step), where step is the spacing between samples.
+    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
+        There are num equally spaced samples in the closed interval
+        `[start, stop]` or the half-open interval `[start, stop)`
+        (depending on whether endpoint is True or False).
+    step : float, optional
+        Only returned if retstep is True
+        Size of spacing between samples.
+
+    Notes
+    -----
+    This function currently does not support ``start`` and ``stop`` as ndarrays and
+    axis could only be 0 now.
+    """
+    if isinstance(start, (list, _np.ndarray, NDArray)) or \
+        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")
+    ctx = kwargs.pop('ctx', current_context())
+    if ctx is None:
+        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)
+    else:
+        return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)

python/mxnet/numpy/multiarray.py

@@ -45,7 +45,7 @@
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'maximum', 'minimum', 'stack', 'arange',
            'argmax', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'concatenate',
-           'clip', 'split', 'swapaxes', 'expand_dims', 'tile']
+           'clip', 'split', 'swapaxes', 'expand_dims', 'tile', 'linspace']
 
 
 # This function is copied from ndarray.py since pylint
@@ -1791,3 +1791,46 @@ def tile(A, reps):
         The tiled output array.
     """
     return _npi.tile(A, reps)
+
+
+@set_module('mxnet.numpy')
+def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, **kwargs):
+    """Return evenly spaced numbers over a specified interval.
+
+    Returns num evenly spaced samples, calculated over the interval [start, stop].
+    The endpoint of the interval can optionally be excluded.
+
+    Parameters
+    ----------
+    start : array_like
+        The starting value of the sequence.
+    stop : array_like
+        The end value of the sequence, unless endpoint is set to False. In
+        that case, the sequence consists of all but the last of num + 1
+        evenly spaced samples, so that stop is excluded. Note that the step
+        size changes when endpoint is False.
+    num : int, optional
+        Number of samples to generate. Default is 50. Must be non-negative.
+    endpoint : bool, optional
+        If True, stop is the last sample. Otherwise, it is not included.
+        Default is True.
+    retstep: bool, optional
+        If True, return (samples, step), where step is the spacing between samples.
+    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
+        There are num equally spaced samples in the closed interval
+        `[start, stop]` or the half-open interval `[start, stop)`
+        (depending on whether endpoint is True or False).
+    step : float, optional
+        Only returned if retstep is True
+        Size of spacing between samples.
+    """
+    return _mx_nd_np.linspace(start, stop, num, endpoint, retstep, dtype, axis, **kwargs)

python/mxnet/symbol/numpy/_symbol.py

@@ -31,7 +31,7 @@
 
 __all__ = ['zeros', 'ones', 'maximum', 'minimum', 'stack', 'concatenate', 'arange', 'argmax',
            'clip', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'split', 'swapaxes',
-           'expand_dims', 'tile']
+           'expand_dims', 'tile', 'linspace']
 
 
 def _num_outputs(sym):
@@ -1307,4 +1307,64 @@ def tile(A, reps):
     return _npi.tile(A, reps)
 
 
+@set_module('mxnet.symbol.numpy')
+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].
+    The endpoint of the interval can optionally be excluded.
+
+    Parameters
+    ----------
+    start : array_like
+        The starting value of the sequence.
+    stop : array_like
+        The end value of the sequence, unless endpoint is set to False. In
+        that case, the sequence consists of all but the last of num + 1
+        evenly spaced samples, so that stop is excluded. Note that the step
+        size changes when endpoint is False.
+    num : int, optional
+        Number of samples to generate. Default is 50. Must be non-negative.
+    endpoint : bool, optional
+        If True, stop is the last sample. Otherwise, it is not included.
+        Default is True.
+    retstep: bool, optional
+        If True, return (samples, step), where step is the spacing between samples.
+    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
+        There are num equally spaced samples in the closed interval
+        `[start, stop]` or the half-open interval `[start, stop)`
+        (depending on whether endpoint is True or False).
+    step : float, optional
+        Only returned if retstep is True
+        Size of spacing between samples.
+
+    Notes
+    -----
+    This function currently does not support ``start`` and ``stop`` as ndarrays and
+    axis could only be 0 now.
+    """
+    if isinstance(start, (list, _np.ndarray)) or \
+        isinstance(stop, (list, _np.ndarray)):
+        raise NotImplementedError('start and stop only support int')
+    if axis != 0:
+        raise NotImplementedError("the function only support axis 0")
+    ctx = kwargs.pop('ctx', current_context())
+    if ctx is None:
+        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)
+    else:
+        return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
+
+
 _set_np_symbol_class(_Symbol)

src/operator/tensor/init_op.cc

@@ -101,6 +101,7 @@ NNVM_REGISTER_OP(_arange)
 .add_arguments(RangeParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_linspace)
+.add_alias("_npi_linspace")
 .describe("Return evenly spaced numbers over a specified interval. Similar to Numpy")
 .set_num_inputs(0)
 .set_num_outputs(1)

tests/python/unittest/test_numpy_op.py

@@ -20,10 +20,11 @@
 import numpy as _np
 import mxnet as mx
 from mxnet import np, npx
+from mxnet.base import MXNetError
 from mxnet.gluon import HybridBlock
 from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray
 from mxnet.test_utils import check_numeric_gradient
-from common import with_seed
+from common import assertRaises, with_seed
 import random
 
 
@@ -554,6 +555,76 @@ def hybrid_forward(self, F, x):
                 assert same(mx_out.asnumpy(), np_out)
 
 
+@with_seed()
+@npx.use_np_shape
+def test_np_linspace():
+    configs = [
+        (0.0, 1.0, 10),
+        (-2, 4, 30),
+        (5.234324, 8.98324, 324),
+        (2, 10, 100)
+    ]
+    exception_configs = [
+        (0, 10, -1),
+        (0, 1, 2.5)
+    ]
+    dtypes = ['int32', 'float16', 'float32', 'float64', None]
+    for config in configs:
+        for dtype in dtypes:
+            for endpoint in [False, True]:
+                for retstep in [False, True]:
+                    if isinstance(config, tuple):
+                        mx_ret = np.linspace(*config, endpoint=endpoint, retstep=retstep, dtype=dtype)
+                        np_ret = _np.linspace(*config, endpoint=endpoint, retstep=retstep, dtype=dtype)
+                    else:
+                        mx_ret = np.linspace(config, endpoint=endpoint, retstep=retstep, dtype=dtype)
+                        np_ret = _np.linspace(config, endpoint=endpoint, retstep=retstep, dtype=dtype)
+                    if retstep:
+                        assert_almost_equal(mx_ret[0].asnumpy(), np_ret[0], atol=1e-3, rtol=1e-5)
+                        same(mx_ret[1], np_ret[1])
+                    else:
+                        assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-3, rtol=1e-5)
+    # check for exception input
+    for config in exception_configs:
+        assertRaises(MXNetError, np.linspace, *config)
+    # check linspace equivalent to arange
+    for test_index in range(1000):
+        assert_almost_equal(mx.np.linspace(0, test_index, test_index + 1).asnumpy(), mx.np.arange(test_index + 1).asnumpy())
+    @npx.use_np
+    class TestLinspace(HybridBlock):
+        def __init__(self, start, stop, num=50, endpoint=None, retstep=False, dtype=None, axis=0):
+            super(TestLinspace, self).__init__()
+            self._start = start
+            self._stop = stop
+            self._num = num
+            self._endpoint = endpoint
+            self._retstep = retstep
+            self._dtype = dtype
+
+        def hybrid_forward(self, F, x):
+            if self._retstep:
+                raise ValueError("linspace didn't support retstep = True inside HybridBlock")
+            else:
+                return x + F.np.linspace(self._start, self._stop, self._num, \
+                self._endpoint, self._retstep, self._dtype)
+
+    for dtype in dtypes:
+        x = np.zeros(shape=(), dtype=dtype)
+        for config in configs:
+            for hybridize in [False, True]:
+                for endpoint in [False, True]:
+                    if isinstance(config, tuple):
+                        net = TestLinspace(*config, endpoint=endpoint, dtype=dtype)
+                        np_out = _np.linspace(*config, endpoint=endpoint, dtype=dtype)
+                    else:
+                        net = TestLinspace(config, endpoint=endpoint, dtype=dtype)
+                        np_out = _np.linspace(config, endpoint=endpoint, dtype=dtype)
+                    if hybridize:
+                        net.hybridize()
+                    mx_out = net(x)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-3, rtol=1e-5)
+
+
 @with_seed()
 @npx.use_np_shape
 def test_np_argmax():