Source code for mxnet.ndarray.ndarray

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# coding: utf-8
# pylint: disable=too-many-lines, protected-access
# pylint: disable=import-error, no-name-in-module, undefined-variable
"""NDArray API of MXNet."""
from __future__ import absolute_import
from __future__ import division

try:
    from __builtin__ import slice as py_slice
except ImportError:
    from builtins import slice as py_slice

import ctypes
import warnings
import operator
from functools import reduce # pylint: disable=redefined-builtin
import numpy as np
from ..base import _LIB, numeric_types, integer_types
from ..base import c_array, mx_real_t
from ..base import mx_uint, NDArrayHandle, check_call
from ..base import ctypes2buffer
from ..context import Context
from . import _internal
from . import op
from ._internal import NDArrayBase

__all__ = ["NDArray", "concatenate", "_DTYPE_NP_TO_MX", "_DTYPE_MX_TO_NP", "_GRAD_REQ_MAP",
           "ones", "add", "arange", "divide", "equal", "full", "greater", "greater_equal",
           "imdecode", "lesser", "lesser_equal", "maximum", "minimum", "moveaxis", "modulo",
           "multiply", "not_equal", "onehot_encode", "power", "subtract", "true_divide",
           "waitall", "_new_empty_handle"]

_STORAGE_TYPE_UNDEFINED = -1
_STORAGE_TYPE_DEFAULT = 0
_STORAGE_TYPE_ROW_SPARSE = 1
_STORAGE_TYPE_CSR = 2

# pylint: disable= no-member
_DTYPE_NP_TO_MX = {
    None: -1,
    np.float32: 0,
    np.float64: 1,
    np.float16: 2,
    np.uint8: 3,
    np.int32: 4,
    np.int8: 5,
    np.int64: 6,
}

_DTYPE_MX_TO_NP = {
    -1: None,
    0: np.float32,
    1: np.float64,
    2: np.float16,
    3: np.uint8,
    4: np.int32,
    5: np.int8,
    6: np.int64,
}

_STORAGE_TYPE_STR_TO_ID = {
    'undefined': _STORAGE_TYPE_UNDEFINED,
    'default': _STORAGE_TYPE_DEFAULT,
    'row_sparse': _STORAGE_TYPE_ROW_SPARSE,
    'csr': _STORAGE_TYPE_CSR,
}

_STORAGE_TYPE_ID_TO_STR = {
    _STORAGE_TYPE_UNDEFINED: 'undefined',
    _STORAGE_TYPE_DEFAULT: 'default',
    _STORAGE_TYPE_ROW_SPARSE: 'row_sparse',
    _STORAGE_TYPE_CSR: 'csr',
}

_GRAD_REQ_MAP = {
    'null': 0,
    'write': 1,
    'add': 3
}
# pylint: enable= no-member


def _new_empty_handle():
    """Returns a new empty handle.

    Empty handle can be used to hold a result.

    Returns
    -------
    handle
        A new empty `NDArray` handle.
    """
    hdl = NDArrayHandle()
    check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl)))
    return hdl


def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
    """Return a new handle with specified shape and context.

    Empty handle is only used to hold results.

    Returns
    -------
    handle
        A new empty `NDArray` handle.
    """
    hdl = NDArrayHandle()
    check_call(_LIB.MXNDArrayCreateEx(
        c_array(mx_uint, shape),
        mx_uint(len(shape)),
        ctypes.c_int(ctx.device_typeid),
        ctypes.c_int(ctx.device_id),
        ctypes.c_int(int(delay_alloc)),
        ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
        ctypes.byref(hdl)))
    return hdl


[docs]def waitall(): """Wait for all async operations to finish in MXNet. This function is used for benchmarking only. """ check_call(_LIB.MXNDArrayWaitAll())
def _storage_type(handle): storage_type = ctypes.c_int(0) check_call(_LIB.MXNDArrayGetStorageType(handle, ctypes.byref(storage_type))) return storage_type.value
[docs]class NDArray(NDArrayBase): """An array object representing a multidimensional, homogeneous array of fixed-size items. """ __slots__ = [] # make numpy functions return NDArray instead of numpy object array __array_priority__ = 1000.0 # pylint: disable= no-member, undefined-variable
[docs] def __repr__(self): """Returns a string representation of the array.""" shape_info = 'x'.join(['%d' % x for x in self.shape]) return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()), self.__class__.__name__, shape_info, self.context)
def __reduce__(self): return NDArray, (None,), self.__getstate__()
[docs] def __add__(self, other): """x.__add__(y) <=> x+y <=> mx.nd.add(x, y) """ return add(self, other)
[docs] def __iadd__(self, other): """x.__iadd__(y) <=> x+=y """ if not self.writable: raise ValueError('trying to add to a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_add(self, other, out=self) elif isinstance(other, numeric_types): return _internal._plus_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))
def __radd__(self, other): return self.__add__(other)
[docs] def __sub__(self, other): """x.__sub__(y) <=> x-y <=> mx.nd.subtract(x, y) """ return subtract(self, other)
[docs] def __isub__(self, other): """x.__isub__(y) <=> x-=y """ if not self.writable: raise ValueError('trying to subtract from a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_sub(self, other, out=self) elif isinstance(other, numeric_types): return _internal._minus_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))
[docs] def __rsub__(self, other): """x.__rsub__(y) <=> y-x <=> mx.nd.subtract(y, x) """ return subtract(other, self)
[docs] def __mul__(self, other): """x.__mul__(y) <=> x*y <=> mx.nd.multiply(x, y) """ return multiply(self, other)
[docs] def __neg__(self): """x.__neg__(y) <=> -x """ return _internal._mul_scalar(self, -1.0)
[docs] def __imul__(self, other): """x.__imul__(y) <=> x*=y """ if not self.writable: raise ValueError('trying to multiply to a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_mul(self, other, out=self) elif isinstance(other, numeric_types): return _internal._mul_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))
def __rmul__(self, other): return self.__mul__(other)
[docs] def __div__(self, other): """x.__div__(y) <=> x/y <=> mx.nd.divide(x, y) """ return divide(self, other)
[docs] def __rdiv__(self, other): """x.__rdiv__(y) <=> y/x <=> mx.nd.divide(y, x) """ return divide(other, self)
[docs] def __idiv__(self, other): """x.__rdiv__(y) <=> x/=y """ if not self.writable: raise ValueError('trying to divide from a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_div(self, other, out=self) elif isinstance(other, numeric_types): return _internal._div_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))
def __truediv__(self, other): return divide(self, other) def __rtruediv__(self, other): return divide(other, self) def __itruediv__(self, other): return self.__idiv__(other)
[docs] def __mod__(self, other): """x.__mod__(y) <=> x%y <=> mx.nd.modulo(x, y) """ return modulo(self, other)
[docs] def __rmod__(self, other): """x.__rmod__(y) <=> y%x <=> mx.nd.modulo(y, x) """ return modulo(other, self)
[docs] def __imod__(self, other): """x.__rmod__(y) <=> x%=y """ if not self.writable: raise ValueError('trying to take modulo from a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_mod(self, other, out=self) elif isinstance(other, numeric_types): return _internal._mod_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other)))
[docs] def __pow__(self, other): """x.__pow__(y) <=> x**y <=> mx.nd.power(x,y) """ return power(self, other)
[docs] def __rpow__(self, other): """x.__pow__(y) <=> y**x <=> mx.nd.power(y,x) """ return power(other, self)
[docs] def __eq__(self, other): """x.__eq__(y) <=> x==y <=> mx.nd.equal(x, y) """ return equal(self, other)
[docs] def __ne__(self, other): """x.__ne__(y) <=> x!=y <=> mx.nd.not_equal(x, y) """ return not_equal(self, other)
[docs] def __gt__(self, other): """x.__gt__(y) <=> x>y <=> mx.nd.greater(x, y) """ return greater(self, other)
[docs] def __ge__(self, other): """x.__ge__(y) <=> x>=y <=> mx.nd.greater_equal(x, y) """ return greater_equal(self, other)
[docs] def __lt__(self, other): """x.__lt__(y) <=> x mx.nd.lesser(x, y) """ return lesser(self, other)
[docs] def __le__(self, other): """x.__le__(y) <=> x<=y <=> mx.nd.less_equal(x, y) """ return lesser_equal(self, other)
def __bool__(self): num_elements = reduce(operator.mul, self.shape, 1) if num_elements == 0: return False elif num_elements == 1: return bool(self.asscalar()) else: raise ValueError("The truth value of an NDArray with multiple elements " \ "is ambiguous.") __nonzero__ = __bool__
[docs] def __len__(self): """Number of element along the first axis.""" return self.shape[0]
def __getstate__(self): handle = self.handle this = {'handle' : None} if handle is not None: length = ctypes.c_size_t() cptr = ctypes.POINTER(ctypes.c_char)() check_call(_LIB.MXNDArraySaveRawBytes(self.handle, ctypes.byref(length), ctypes.byref(cptr))) this['handle'] = ctypes2buffer(cptr, length.value) return this def __setstate__(self, state): # pylint: disable=assigning-non-slot handle = state['handle'] if handle is not None: buf = handle handle = NDArrayHandle() ptr = (ctypes.c_char * len(buf)).from_buffer(buf) length = ctypes.c_size_t(len(buf)) check_call(_LIB.MXNDArrayLoadFromRawBytes(ptr, length, ctypes.byref(handle))) self.handle = handle else: self.handle = None
[docs] def __setitem__(self, key, value): """x.__setitem__(i, y) <=> x[i]=y Set self[key] to value. Parameters ---------- key : int, slice or tuple The indexing key. value : scalar, NDArray or numpy.ndarray The value to set. Examples -------- >>> x = mx.nd.zeros((2,3)) >>> x[:] = 1 >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> x[:,1:2] = 2 >>> x.asnumpy() array([[ 1., 2., 1.], [ 1., 2., 1.]], dtype=float32) >>> x[1:2,1:] = 3 >>> x.asnumpy() array([[ 1., 2., 1.], [ 1., 3., 3.]], dtype=float32) >>> x[1:,0:2] = mx.nd.zeros((1,2)) >>> x.asnumpy() array([[ 1., 2., 1.], [ 0., 0., 3.]], dtype=float32) >>> x[1,2] = 4 >>> x.asnumpy() array([[ 1., 2., 1.], [ 0., 0., 4.]], dtype=float32) """ # pylint: disable=too-many-branches if not self.writable: raise ValueError('Cannot assign to readonly NDArray') if isinstance(key, integer_types): sliced_arr = self._at(key) sliced_arr[:] = value return elif isinstance(key, py_slice): if key.step is not None: raise ValueError('NDArray only supports slicing with step size 1') if key.start is not None or key.stop is not None: sliced_arr = self._slice(key.start, key.stop) sliced_arr[:] = value return if isinstance(value, NDArray): if value.handle is not self.handle: value.copyto(self) elif isinstance(value, numeric_types): _internal._set_value(float(value), out=self) elif isinstance(value, (np.ndarray, np.generic)): self._sync_copyfrom(value) else: raise TypeError( 'NDArray does not support assignment with %s of type %s'%( str(value), str(type(value)))) elif isinstance(key, tuple): # multi-dimension indexing my_shape = self.shape assert len(key) <= len(my_shape), \ "Indexing dimensions exceed array dimensions, %d vs %d"%( len(key), len(my_shape)) begin = [0 for _ in my_shape] end = [x for x in my_shape] expand = [] for i, slice_i in enumerate(key): if isinstance(slice_i, integer_types): assert slice_i < my_shape[i] begin[i] = slice_i end[i] = slice_i + 1 expand.append(i) elif isinstance(slice_i, py_slice): # only support continuous slicing assert slice_i.step is None, \ "NDArray only supports slicing with step size 1." begin[i] = slice_i.start or 0 end[i] = slice_i.stop or my_shape[i] assert begin[i] < end[i] assert end[i] <= my_shape[i] else: raise ValueError( "NDArray does not support slicing with key %s of type %s."%( str(slice_i), str(type(slice_i)))) if isinstance(value, NDArray): value = value.as_in_context(self.context) self._slice_assign(value, begin, end, expand) elif isinstance(value, numeric_types): _internal._crop_assign_scalar(self, out=self, begin=begin, end=end, scalar=value) elif isinstance(value, (np.ndarray, np.generic)): value = array(value, ctx=self.context, dtype=self.dtype) self._slice_assign(value, begin, end, expand) else: raise TypeError( 'NDArray does not support assignment with %s of type %s'%( str(value), str(type(value)))) else: raise ValueError( "NDArray does not support slicing with key %s of type %s."%( str(key), str(type(key))))
# pylint: enable=too-many-branches def _slice_assign(self, value, begin, end, expand): vshape = list(value.shape) if expand and len(vshape) != len(begin): if len(expand) + len(vshape) != len(begin): sshape = [e - b for e, b in zip(end, begin)] for i in reversed(expand): sshape.pop(i) raise ValueError( "Cannot assign NDArray with shape %s to NDArray slice with " \ "shape %s"%(str(vshape), str(sshape))) for i in expand: vshape.insert(i, 1) value = value.reshape(vshape) _internal._crop_assign(self, value, out=self, begin=begin, end=end)
[docs] def __getitem__(self, key): """x.__getitem__(i) <=> x[i] Returns a sliced view of this array. Parameters ---------- key : int or slice, or array like Indexing key. Examples -------- >>> x = mx.nd.arange(0,6).reshape((2,3)) >>> x.asnumpy() array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> x[1].asnumpy() array([ 3., 4., 5.], dtype=float32) >>> y = x[0:1] >>> y[:] = 2 >>> x.asnumpy() array([[ 2., 2., 2.], [ 3., 4., 5.]], dtype=float32) """ # multi-dimensional slicing is not supported yet if isinstance(key, integer_types): if key > self.shape[0] - 1: raise IndexError( 'index {} is out of bounds for axis 0 with size {}'.format( key, self.shape[0])) return self._at(key) elif isinstance(key, py_slice): if key.step is not None: raise ValueError("NDArray only supports slicing with step size 1.") if key.start is not None or key.stop is not None: return self._slice(key.start, key.stop) return self elif isinstance(key, tuple): shape = self.shape assert len(key) > 0, "Cannot slice with empty indices" assert len(shape) >= len(key), \ "Slicing dimensions exceeds array dimensions, %d vs %d"%( len(key), len(shape)) if isinstance(key[0], (NDArray, np.ndarray, list, tuple)): indices = [] dtype = 'int32' shape = None for idx_i in key: if not isinstance(idx_i, NDArray): assert isinstance(idx_i, (NDArray, np.ndarray, list, tuple)), \ "Combining basic and advanced indexing is not supported " \ "yet. Indices must be all NDArray or all slice, not a " \ "mix of both." idx_i = array(idx_i, ctx=self.context, dtype=dtype) else: dtype = idx_i.dtype if shape is None: shape = idx_i.shape else: assert shape == idx_i.shape, \ "All index arrays must have the same shape: %s vs %s. " \ "Broadcasting is not supported yet."%(shape, idx_i.shape) indices.append(idx_i) indices = op.stack(*indices) return op.gather_nd(self, indices) else: oshape = [] begin = [] end = [] i = -1 for i, slice_i in enumerate(key): if isinstance(slice_i, integer_types): begin.append(slice_i) end.append(slice_i+1) elif isinstance(slice_i, py_slice): if slice_i.step is not None: raise ValueError("NDArray only supports slicing with step size 1.") begin.append(0 if slice_i.start is None else slice_i.start) end.append(shape[i] if slice_i.stop is None else slice_i.stop) oshape.append(end[i] - begin[i]) elif isinstance(slice_i, (NDArray, np.ndarray, list, tuple)): raise ValueError( "Combining basic and advanced indexing is not supported " \ "yet. Indices must be all NDArray or all slice, not a " \ "mix of both.") else: raise ValueError( "NDArray does not support slicing with key %s of type %s."%( str(slice_i), str(type(slice_i)))) oshape.extend(shape[i+1:]) if len(oshape) == 0: oshape.append(1) return op.slice(self, begin, end).reshape(oshape) else: raise ValueError( "NDArray does not support slicing with key %s of type %s."%( str(key), str(type(key))))
def _sync_copyfrom(self, source_array): """Performs a synchronized copy from the `source_array` to the current array. This is called through ``x[:] = source_array``, where the `source_array` is a `numpy.ndarray` or array-like object. This function blocks until all the pending read/write operations with respect to the current `NDArray` are finished and carry out the copy operation to the current NDArray. Parameters ---------- source_array : array_like The data source we would like to copy from. Example ------- >>> a = mx.nd.array([1, 2]) >>> a.asnumpy() array([ 1., 2.], dtype=float32) >>> a[:] = np.array([3, 4]) >> a.asnumpy() array([ 3., 4.], dtype=float32) """ if not isinstance(source_array, np.ndarray): try: source_array = np.array(source_array, dtype=self.dtype) except: raise TypeError('array must consist of array-like data,' + 'type %s is not supported' % str(type(array))) source_array = np.ascontiguousarray(source_array, dtype=self.dtype) if source_array.shape != self.shape: raise ValueError('Shape inconsistent: expected %s vs got %s'%( str(self.shape), str(source_array.shape))) check_call(_LIB.MXNDArraySyncCopyFromCPU( self.handle, source_array.ctypes.data_as(ctypes.c_void_p), ctypes.c_size_t(source_array.size))) def _slice(self, start, stop): """Returns a sliced NDArray that shares memory with the current one. This is called through ``x[start:stop]``. Parameters ---------- start : int Starting inclusive index of slice in the first dim. stop : int Finishing exclusive index of slice in the first dim. Returns ------- `NDArray` sharing the memory with the current one sliced from start to stop in the first dim. Examples: >>> a = mx.nd.array([[1,2], [3, 4], [5, 6], [7, 8]]) >>> a[1:2].asnumpy() array([[ 3., 4.]], dtype=float32) >>> a[1:1].asnumpy() array([], shape=(0, 2), dtype=float32) """ handle = NDArrayHandle() if start is None: start = mx_uint(0) elif start < 0: length = self.shape[0] start += length assert start >= 0, "Slicing start %d exceeds limit of %d"%(start-length, length) start = mx_uint(start) else: start = mx_uint(start) if stop is None: stop = mx_uint(self.shape[0]) elif stop < 0: length = self.shape[0] stop += length assert stop >= 0, "Slicing end %d exceeds limit of %d"%(stop-length, length) stop = mx_uint(stop) else: stop = mx_uint(stop) check_call(_LIB.MXNDArraySlice( self.handle, start, stop, ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable) def _at(self, idx): """Returns a view of the array sliced at `idx` in the first dim. This is called through ``x[idx]``. Parameters ---------- idx : int index for slicing the `NDArray` in the first dim. Returns ------- NDArray `NDArray` sharing the memory with the current one sliced at `idx` in the first dim. Examples -------- >>> a = mx.nd.array([[1,2], [3, 4]]) >>> a[1].asnumpy() array([ 3., 4.], dtype=float32) >>> b = mx.nd.array([1, 2, 3, 4]) >>> b[0].asnumpy() array([ 1.], dtype=float32) """ handle = NDArrayHandle() idx = mx_uint(idx) check_call(_LIB.MXNDArrayAt( self.handle, idx, ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable)
[docs] def reshape(self, shape): """Returns a **view** of this array with a new shape without altering any data. Parameters ---------- shape : tuple of int The new shape should not change the array size, namely ``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``. One dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions. 0 Dimensions in shape will be copied from original shape, i.e. if x.shape == (3, 4, 5), x.reshape((0, 20)).shape will be (3, 20). Returns ------- NDArray An array with desired shape that shares data with this array. Examples -------- >>> x = mx.nd.arange(0,6).reshape((2,3)) >>> x.asnumpy() array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> y = x.reshape((3,2)) >>> y.asnumpy() array([[ 0., 1.], [ 2., 3.], [ 4., 5.]], dtype=float32) >>> y = x.reshape((3,-1)) >>> y.asnumpy() array([[ 0., 1.], [ 2., 3.], [ 4., 5.]], dtype=float32) >>> y[:] = -1 >>> x.asnumpy() array([[-1., -1., -1.], [-1., -1., -1.]], dtype=float32) """ handle = NDArrayHandle() # Actual reshape check_call(_LIB.MXNDArrayReshape(self.handle, len(shape), c_array(ctypes.c_int, shape), ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable)
[docs] def reshape_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`reshape_like`. The arguments are the same as for :py:func:`reshape_like`, with this array as data. """ return op.reshape_like(self, *args, **kwargs)
[docs] def zeros_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`zeros_like`. The arguments are the same as for :py:func:`zeros_like`, with this array as data. """ return op.zeros_like(self, *args, **kwargs)
[docs] def ones_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`ones_like`. The arguments are the same as for :py:func:`ones_like`, with this array as data. """ return op.ones_like(self, *args, **kwargs)
[docs] def broadcast_axes(self, *args, **kwargs): """Convenience fluent method for :py:func:`broadcast_axes`. The arguments are the same as for :py:func:`broadcast_axes`, with this array as data. """ return op.broadcast_axes(self, *args, **kwargs)
[docs] def repeat(self, *args, **kwargs): """Convenience fluent method for :py:func:`repeat`. The arguments are the same as for :py:func:`repeat`, with this array as data. """ return op.repeat(self, *args, **kwargs)
[docs] def pad(self, *args, **kwargs): """Convenience fluent method for :py:func:`pad`. The arguments are the same as for :py:func:`pad`, with this array as data. """ return op.pad(self, *args, **kwargs)
[docs] def swapaxes(self, *args, **kwargs): """Convenience fluent method for :py:func:`swapaxes`. The arguments are the same as for :py:func:`swapaxes`, with this array as data. """ return op.swapaxes(self, *args, **kwargs)
[docs] def split(self, *args, **kwargs): """Convenience fluent method for :py:func:`split`. The arguments are the same as for :py:func:`split`, with this array as data. """ return op.split(self, *args, **kwargs)
[docs] def slice(self, *args, **kwargs): """Convenience fluent method for :py:func:`slice`. The arguments are the same as for :py:func:`slice`, with this array as data. """ return op.slice(self, *args, **kwargs)
[docs] def slice_axis(self, *args, **kwargs): """Convenience fluent method for :py:func:`slice_axis`. The arguments are the same as for :py:func:`slice_axis`, with this array as data. """ return op.slice_axis(self, *args, **kwargs)
[docs] def take(self, *args, **kwargs): """Convenience fluent method for :py:func:`take`. The arguments are the same as for :py:func:`take`, with this array as data. """ return op.take(self, *args, **kwargs)
[docs] def one_hot(self, *args, **kwargs): """Convenience fluent method for :py:func:`one_hot`. The arguments are the same as for :py:func:`one_hot`, with this array as data. """ return op.one_hot(self, *args, **kwargs)
[docs] def pick(self, *args, **kwargs): """Convenience fluent method for :py:func:`pick`. The arguments are the same as for :py:func:`pick`, with this array as data. """ return op.pick(self, *args, **kwargs)
[docs] def sort(self, *args, **kwargs): """Convenience fluent method for :py:func:`sort`. The arguments are the same as for :py:func:`sort`, with this array as data. """ return op.sort(self, *args, **kwargs)
[docs] def topk(self, *args, **kwargs): """Convenience fluent method for :py:func:`topk`. The arguments are the same as for :py:func:`topk`, with this array as data. """ return op.topk(self, *args, **kwargs)
[docs] def argsort(self, *args, **kwargs): """Convenience fluent method for :py:func:`argsort`. The arguments are the same as for :py:func:`argsort`, with this array as data. """ return op.argsort(self, *args, **kwargs)
[docs] def argmax(self, *args, **kwargs): """Convenience fluent method for :py:func:`argmax`. The arguments are the same as for :py:func:`argmax`, with this array as data. """ return op.argmax(self, *args, **kwargs)
[docs] def argmax_channel(self, *args, **kwargs): """Convenience fluent method for :py:func:`argmax_channel`. The arguments are the same as for :py:func:`argmax_channel`, with this array as data. """ return op.argmax_channel(self, *args, **kwargs)
[docs] def argmin(self, *args, **kwargs): """Convenience fluent method for :py:func:`argmin`. The arguments are the same as for :py:func:`argmin`, with this array as data. """ return op.argmin(self, *args, **kwargs)
[docs] def clip(self, *args, **kwargs): """Convenience fluent method for :py:func:`clip`. The arguments are the same as for :py:func:`clip`, with this array as data. """ return op.clip(self, *args, **kwargs)
[docs] def abs(self, *args, **kwargs): """Convenience fluent method for :py:func:`abs`. The arguments are the same as for :py:func:`abs`, with this array as data. """ return op.abs(self, *args, **kwargs)
[docs] def sign(self, *args, **kwargs): """Convenience fluent method for :py:func:`sign`. The arguments are the same as for :py:func:`sign`, with this array as data. """ return op.sign(self, *args, **kwargs)
[docs] def flatten(self, *args, **kwargs): """Convenience fluent method for :py:func:`flatten`. The arguments are the same as for :py:func:`flatten`, with this array as data. """ return op.flatten(self, *args, **kwargs)
[docs] def expand_dims(self, *args, **kwargs): """Convenience fluent method for :py:func:`expand_dims`. The arguments are the same as for :py:func:`expand_dims`, with this array as data. """ return op.expand_dims(self, *args, **kwargs)
[docs] def tile(self, *args, **kwargs): """Convenience fluent method for :py:func:`tile`. The arguments are the same as for :py:func:`tile`, with this array as data. """ return op.tile(self, *args, **kwargs)
[docs] def transpose(self, *args, **kwargs): """Convenience fluent method for :py:func:`transpose`. The arguments are the same as for :py:func:`transpose`, with this array as data. """ return op.transpose(self, *args, **kwargs)
[docs] def flip(self, *args, **kwargs): """Convenience fluent method for :py:func:`flip`. The arguments are the same as for :py:func:`flip`, with this array as data. """ return op.flip(self, *args, **kwargs)
[docs] def sum(self, *args, **kwargs): """Convenience fluent method for :py:func:`sum`. The arguments are the same as for :py:func:`sum`, with this array as data. """ return op.sum(self, *args, **kwargs)
[docs] def nansum(self, *args, **kwargs): """Convenience fluent method for :py:func:`nansum`. The arguments are the same as for :py:func:`nansum`, with this array as data. """ return op.nansum(self, *args, **kwargs)
[docs] def prod(self, *args, **kwargs): """Convenience fluent method for :py:func:`prod`. The arguments are the same as for :py:func:`prod`, with this array as data. """ return op.prod(self, *args, **kwargs)
[docs] def nanprod(self, *args, **kwargs): """Convenience fluent method for :py:func:`nanprod`. The arguments are the same as for :py:func:`nanprod`, with this array as data. """ return op.nanprod(self, *args, **kwargs)
[docs] def mean(self, *args, **kwargs): """Convenience fluent method for :py:func:`mean`. The arguments are the same as for :py:func:`mean`, with this array as data. """ return op.mean(self, *args, **kwargs)
[docs] def max(self, *args, **kwargs): """Convenience fluent method for :py:func:`max`. The arguments are the same as for :py:func:`max`, with this array as data. """ return op.max(self, *args, **kwargs)
[docs] def min(self, *args, **kwargs): """Convenience fluent method for :py:func:`min`. The arguments are the same as for :py:func:`min`, with this array as data. """ return op.min(self, *args, **kwargs)
[docs] def norm(self, *args, **kwargs): """Convenience fluent method for :py:func:`norm`. The arguments are the same as for :py:func:`norm`, with this array as data. """ return op.norm(self, *args, **kwargs)
[docs] def round(self, *args, **kwargs): """Convenience fluent method for :py:func:`round`. The arguments are the same as for :py:func:`round`, with this array as data. """ return op.round(self, *args, **kwargs)
[docs] def rint(self, *args, **kwargs): """Convenience fluent method for :py:func:`rint`. The arguments are the same as for :py:func:`rint`, with this array as data. """ return op.rint(self, *args, **kwargs)
[docs] def fix(self, *args, **kwargs): """Convenience fluent method for :py:func:`fix`. The arguments are the same as for :py:func:`fix`, with this array as data. """ return op.fix(self, *args, **kwargs)
[docs] def floor(self, *args, **kwargs): """Convenience fluent method for :py:func:`floor`. The arguments are the same as for :py:func:`floor`, with this array as data. """ return op.floor(self, *args, **kwargs)
[docs] def ceil(self, *args, **kwargs): """Convenience fluent method for :py:func:`ceil`. The arguments are the same as for :py:func:`ceil`, with this array as data. """ return op.ceil(self, *args, **kwargs)
[docs] def trunc(self, *args, **kwargs): """Convenience fluent method for :py:func:`trunc`. The arguments are the same as for :py:func:`trunc`, with this array as data. """ return op.trunc(self, *args, **kwargs)
[docs] def sin(self, *args, **kwargs): """Convenience fluent method for :py:func:`sin`. The arguments are the same as for :py:func:`sin`, with this array as data. """ return op.sin(self, *args, **kwargs)
[docs] def cos(self, *args, **kwargs): """Convenience fluent method for :py:func:`cos`. The arguments are the same as for :py:func:`cos`, with this array as data. """ return op.cos(self, *args, **kwargs)
[docs] def tan(self, *args, **kwargs): """Convenience fluent method for :py:func:`tan`. The arguments are the same as for :py:func:`tan`, with this array as data. """ return op.tan(self, *args, **kwargs)
[docs] def arcsin(self, *args, **kwargs): """Convenience fluent method for :py:func:`arcsin`. The arguments are the same as for :py:func:`arcsin`, with this array as data. """ return op.arcsin(self, *args, **kwargs)
[docs] def arccos(self, *args, **kwargs): """Convenience fluent method for :py:func:`arccos`. The arguments are the same as for :py:func:`arccos`, with this array as data. """ return op.arccos(self, *args, **kwargs)
[docs] def arctan(self, *args, **kwargs): """Convenience fluent method for :py:func:`arctan`. The arguments are the same as for :py:func:`arctan`, with this array as data. """ return op.arctan(self, *args, **kwargs)
[docs] def degrees(self, *args, **kwargs): """Convenience fluent method for :py:func:`degrees`. The arguments are the same as for :py:func:`degrees`, with this array as data. """ return op.degrees(self, *args, **kwargs)
[docs] def radians(self, *args, **kwargs): """Convenience fluent method for :py:func:`radians`. The arguments are the same as for :py:func:`radians`, with this array as data. """ return op.radians(self, *args, **kwargs)
[docs] def sinh(self, *args, **kwargs): """Convenience fluent method for :py:func:`sinh`. The arguments are the same as for :py:func:`sinh`, with this array as data. """ return op.sinh(self, *args, **kwargs)
[docs] def cosh(self, *args, **kwargs): """Convenience fluent method for :py:func:`cosh`. The arguments are the same as for :py:func:`cosh`, with this array as data. """ return op.cosh(self, *args, **kwargs)
[docs] def tanh(self, *args, **kwargs): """Convenience fluent method for :py:func:`tanh`. The arguments are the same as for :py:func:`tanh`, with this array as data. """ return op.tanh(self, *args, **kwargs)
[docs] def arcsinh(self, *args, **kwargs): """Convenience fluent method for :py:func:`arcsinh`. The arguments are the same as for :py:func:`arcsinh`, with this array as data. """ return op.arcsinh(self, *args, **kwargs)
[docs] def arccosh(self, *args, **kwargs): """Convenience fluent method for :py:func:`arccosh`. The arguments are the same as for :py:func:`arccosh`, with this array as data. """ return op.arccosh(self, *args, **kwargs)
[docs] def arctanh(self, *args, **kwargs): """Convenience fluent method for :py:func:`arctanh`. The arguments are the same as for :py:func:`arctanh`, with this array as data. """ return op.arctanh(self, *args, **kwargs)
[docs] def exp(self, *args, **kwargs): """Convenience fluent method for :py:func:`exp`. The arguments are the same as for :py:func:`exp`, with this array as data. """ return op.exp(self, *args, **kwargs)
[docs] def expm1(self, *args, **kwargs): """Convenience fluent method for :py:func:`expm1`. The arguments are the same as for :py:func:`expm1`, with this array as data. """ return op.expm1(self, *args, **kwargs)
[docs] def log(self, *args, **kwargs): """Convenience fluent method for :py:func:`log`. The arguments are the same as for :py:func:`log`, with this array as data. """ return op.log(self, *args, **kwargs)
[docs] def log10(self, *args, **kwargs): """Convenience fluent method for :py:func:`log10`. The arguments are the same as for :py:func:`log10`, with this array as data. """ return op.log10(self, *args, **kwargs)
[docs] def log2(self, *args, **kwargs): """Convenience fluent method for :py:func:`log2`. The arguments are the same as for :py:func:`log2`, with this array as data. """ return op.log2(self, *args, **kwargs)
[docs] def log1p(self, *args, **kwargs): """Convenience fluent method for :py:func:`log1p`. The arguments are the same as for :py:func:`log1p`, with this array as data. """ return op.log1p(self, *args, **kwargs)
[docs] def sqrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`sqrt`. The arguments are the same as for :py:func:`sqrt`, with this array as data. """ return op.sqrt(self, *args, **kwargs)
[docs] def rsqrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`rsqrt`. The arguments are the same as for :py:func:`rsqrt`, with this array as data. """ return op.rsqrt(self, *args, **kwargs)
[docs] def cbrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`cbrt`. The arguments are the same as for :py:func:`cbrt`, with this array as data. """ return op.cbrt(self, *args, **kwargs)
[docs] def rcbrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`rcbrt`. The arguments are the same as for :py:func:`rcbrt`, with this array as data. """ return op.rcbrt(self, *args, **kwargs)
[docs] def square(self, *args, **kwargs): """Convenience fluent method for :py:func:`square`. The arguments are the same as for :py:func:`square`, with this array as data. """ return op.square(self, *args, **kwargs)
[docs] def reciprocal(self, *args, **kwargs): """Convenience fluent method for :py:func:`reciprocal`. The arguments are the same as for :py:func:`reciprocal`, with this array as data. """ return op.reciprocal(self, *args, **kwargs)
[docs] def relu(self, *args, **kwargs): """Convenience fluent method for :py:func:`relu`. The arguments are the same as for :py:func:`relu`, with this array as data. """ return op.relu(self, *args, **kwargs)
[docs] def sigmoid(self, *args, **kwargs): """Convenience fluent method for :py:func:`sigmoid`. The arguments are the same as for :py:func:`sigmoid`, with this array as data. """ return op.sigmoid(self, *args, **kwargs)
[docs] def softmax(self, *args, **kwargs): """Convenience fluent method for :py:func:`softmax`. The arguments are the same as for :py:func:`softmax`, with this array as data. """ return op.softmax(self, *args, **kwargs)
[docs] def log_softmax(self, *args, **kwargs): """Convenience fluent method for :py:func:`log_softmax`. The arguments are the same as for :py:func:`log_softmax`, with this array as data. """ return op.log_softmax(self, *args, **kwargs)
# pylint: disable= undefined-variable
[docs] def broadcast_to(self, shape): """Broadcasts the input array to a new shape. Broadcasting is only allowed on axes with size 1. The new shape cannot change the number of dimensions. For example, you could broadcast from shape (2, 1) to (2, 3), but not from shape (2, 3) to (2, 3, 3). Parameters ---------- shape : tuple of int The shape of the desired array. Returns ------- NDArray A NDArray with the desired shape that is not sharing data with this array, even if the new shape is the same as ``self.shape``. Examples -------- >>> x = mx.nd.arange(0,3).reshape((1,3,1)) >>> x.asnumpy() array([[[ 0.], [ 1.], [ 2.]]], dtype=float32) >>> y = x.broadcast_to((2,3,3)) >>> y.asnumpy() array([[[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]], [[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]]], dtype=float32) """ cur_shape = self.shape err_str = 'operands could not be broadcast together with remapped shapes' \ '[original->remapped]: {} and requested shape {}'.format(cur_shape, shape) if len(shape) < len(cur_shape): raise ValueError(err_str) cur_shape = (1,) * (len(shape) - len(cur_shape)) + cur_shape cur_shape_arr = np.array(cur_shape) broadcasting_axes = np.nonzero(cur_shape_arr != np.array(shape)) if (cur_shape_arr[broadcasting_axes] != 1).any(): raise ValueError(err_str) if cur_shape != self.shape: return op.broadcast_to(self.reshape(cur_shape), shape=shape) else: return op.broadcast_to(self, shape=tuple(shape))
# pylint: enable= undefined-variable
[docs] def wait_to_read(self): """Waits until all previous write operations on the current array are finished. This method guarantees that all previous write operations that pushed into the backend engine for execution are actually finished. Examples -------- >>> import time >>> tic = time.time() >>> a = mx.nd.ones((1000,1000)) >>> b = mx.nd.dot(a, a) >>> print(time.time() - tic) # doctest: +SKIP 0.003854036331176758 >>> b.wait_to_read() >>> print(time.time() - tic) # doctest: +SKIP 0.0893700122833252 """ check_call(_LIB.MXNDArrayWaitToRead(self.handle))
@property def ndim(self): """Returns the number of dimensions of this array Examples -------- >>> x = mx.nd.array([1, 2, 3, 4]) >>> x.ndim 1 >>> x = mx.nd.array([[1, 2], [3, 4]]) >>> x.ndim 2 """ return len(self.shape) @property def shape(self): """Tuple of array dimensions. Examples -------- >>> x = mx.nd.array([1, 2, 3, 4]) >>> x.shape (4L,) >>> y = mx.nd.zeros((2, 3, 4)) >>> y.shape (2L, 3L, 4L) """ ndim = mx_uint() pdata = ctypes.POINTER(mx_uint)() check_call(_LIB.MXNDArrayGetShape( self.handle, ctypes.byref(ndim), ctypes.byref(pdata))) return tuple(pdata[:ndim.value]) @property def size(self): """Number of elements in the array. Equivalent to the product of the array's dimensions. Examples -------- >>> import numpy as np >>> x = mx.nd.zeros((3, 5, 2)) >>> x.size 30 >>> np.prod(x.shape) 30 """ size = 1 for i in self.shape: size *= i return size @property def context(self): """Device context of the array. Examples -------- >>> x = mx.nd.array([1, 2, 3, 4]) >>> x.context cpu(0) >>> type(x.context) >>> y = mx.nd.zeros((2,3), mx.gpu(0)) >>> y.context gpu(0) """ dev_typeid = ctypes.c_int() dev_id = ctypes.c_int() check_call(_LIB.MXNDArrayGetContext( self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id))) return Context(Context.devtype2str[dev_typeid.value], dev_id.value) @property def dtype(self): """Data-type of the array's elements. Returns ------- numpy.dtype This NDArray's data type. Examples -------- >>> x = mx.nd.zeros((2,3)) >>> x.dtype >>> y = mx.nd.zeros((2,3), dtype='int32') >>> y.dtype """ mx_dtype = ctypes.c_int() check_call(_LIB.MXNDArrayGetDType( self.handle, ctypes.byref(mx_dtype))) return _DTYPE_MX_TO_NP[mx_dtype.value] @property def stype(self): """Storage-type of the array. """ return _STORAGE_TYPE_ID_TO_STR[_storage_type(self.handle)] @property # pylint: disable= invalid-name, undefined-variable def T(self): """Returns a copy of the array with axes transposed. Equivalent to ``mx.nd.transpose(self)`` except that self is returned if ``self.ndim < 2``. Unlike ``numpy.ndarray.T``, this function returns a copy rather than a view of the array unless ``self.ndim < 2``. Examples -------- >>> x = mx.nd.arange(0,6).reshape((2,3)) >>> x.asnumpy() array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> x.T.asnumpy() array([[ 0., 3.], [ 1., 4.], [ 2., 5.]], dtype=float32) """ if len(self.shape) < 2: return self return op.transpose(self) # pylint: enable= invalid-name, undefined-variable @property def _fresh_grad(self): """Whether this array's corresponding gradient array (registered via `autograd.mark_variables`) has been updated by `autograd.backward` since last reset. `_fresh_grad` need to be manually set to False after consuming gradient (usually after updating this array). """ out = ctypes.c_int() check_call(_LIB.MXNDArrayGetGradState(self.handle, ctypes.byref(out))) return out.value @_fresh_grad.setter def _fresh_grad(self, state): check_call(_LIB.MXNDArraySetGradState(self.handle, ctypes.c_int(state)))
[docs] def asnumpy(self): """Returns a ``numpy.ndarray`` object with value copied from this array. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.asnumpy() >>> type(y) >>> y array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> z = mx.nd.ones((2,3), dtype='int32') >>> z.asnumpy() array([[1, 1, 1], [1, 1, 1]], dtype=int32) """ data = np.empty(self.shape, dtype=self.dtype) check_call(_LIB.MXNDArraySyncCopyToCPU( self.handle, data.ctypes.data_as(ctypes.c_void_p), ctypes.c_size_t(data.size))) return data
[docs] def asscalar(self): """Returns a scalar whose value is copied from this array. This function is equivalent to ``self.asnumpy()[0]``. This NDArray must have shape (1,). Examples -------- >>> x = mx.nd.ones((1,), dtype='int32') >>> x.asscalar() 1 >>> type(x.asscalar()) """ if self.shape != (1,): raise ValueError("The current array is not a scalar") return self.asnumpy()[0]
[docs] def astype(self, dtype): """Returns a copy of the array after casting to a specified type. Parameters ---------- dtype : numpy.dtype or str The type of the returned array. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The copied array after casting to the specified type. Examples -------- >>> x = mx.nd.zeros((2,3), dtype='float32') >>> y = x.astype('int32') >>> y.dtype """ res = empty(self.shape, ctx=self.context, dtype=dtype) self.copyto(res) return res
[docs] def copyto(self, other): """Copies the value of this array to another array. If ``other`` is a ``NDArray`` object, then ``other.shape`` and ``self.shape`` should be the same. This function copies the value from ``self`` to ``other``. If ``other`` is a context, a new ``NDArray`` will be first created on the target context, and the value of ``self`` is copied. Parameters ---------- other : NDArray or Context The destination array or context. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The copied array. If ``other`` is an ``NDArray``, then the return value and ``other`` will point to the same ``NDArray``. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.zeros((2,3), mx.gpu(0)) >>> z = x.copyto(y) >>> z is y True >>> y.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.copyto(mx.gpu(0)) """ if isinstance(other, NDArray): if other.handle is self.handle: warnings.warn('You are attempting to copy an array to itself', RuntimeWarning) return return _internal._copyto(self, out=other) elif isinstance(other, Context): hret = NDArray(_new_alloc_handle(self.shape, other, True, self.dtype)) return _internal._copyto(self, out=hret) else: raise TypeError('copyto does not support type ' + str(type(other)))
[docs] def copy(self): """Makes a copy of this ``NDArray``, keeping the same context. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The copied array Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.copy() >>> y.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) """ return self.copyto(self.context)
[docs] def as_in_context(self, context): """Returns an array on the target device with the same value as this array. If the target context is the same as ``self.context``, then ``self`` is returned. Otherwise, a copy is made. Parameters ---------- context : Context The target context. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The target array. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.as_in_context(mx.cpu()) >>> y is x True >>> z = x.as_in_context(mx.gpu(0)) >>> z is x False """ if self.context == context: return self return self.copyto(context)
[docs] def attach_grad(self, grad_req='write', stype=None): """Attach a gradient buffer to this NDArray, so that `backward` can compute gradient with respect to it. Parameters ---------- grad_req : {'write', 'add', 'null'} How gradient will be accumulated. - 'write': gradient will be overwritten on every backward. - 'add': gradient will be added to existing value on every backward. - 'null': do not compute gradient for this NDArray. stype : str, optional The storage type of the gradient array. Defaults to the same stype of this NDArray. """ from . import zeros as _zeros if stype is not None: grad = _zeros(self.shape, stype=stype) else: grad = op.zeros_like(self) # pylint: disable=undefined-variable grad_req = _GRAD_REQ_MAP[grad_req] check_call(_LIB.MXAutogradMarkVariables( 1, ctypes.pointer(self.handle), ctypes.pointer(mx_uint(grad_req)), ctypes.pointer(grad.handle)))
@property def grad(self): """Returns gradient buffer attached to this NDArray.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl))) if hdl.value is None: return None return _ndarray_cls(hdl)
[docs] def detach(self): """Returns a new NDArray, detached from the current graph.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl))) return _ndarray_cls(hdl)
[docs] def backward(self, out_grad=None, retain_graph=False, train_mode=True): """Compute the gradients of this NDArray w.r.t variables. Parameters ---------- out_grad : NDArray, optional Gradient with respect to head. retain_graph : bool, optional Whether to retain the computaion graph for another backward pass on the same graph. By default the computaion history is cleared. train_mode : bool, optional Whether to compute gradient for training or inference. """ if out_grad is None: ograd_handles = [NDArrayHandle(0)] else: ograd_handles = [out_grad.handle] check_call(_LIB.MXAutogradBackwardEx( 1, c_array(NDArrayHandle, [self.handle]), c_array(NDArrayHandle, ograd_handles), 0, ctypes.c_void_p(0), ctypes.c_int(retain_graph), ctypes.c_int(0), ctypes.c_int(train_mode), ctypes.c_void_p(0), ctypes.c_void_p(0)))
[docs] def tostype(self, stype): """Return a copy of the array with chosen storage type. See Also ---------- :meth:`mxnet.ndarray.cast_storage`. Returns ------- NDArray, CSRNDArray or RowSparseNDArray A copy of the array with the chosen storage stype """ return op.cast_storage(self, stype=stype)
[docs]def onehot_encode(indices, out): """One-hot encoding indices into matrix out. .. note:: `onehot_encode` is deprecated. Use `one_hot` instead. """ # pylint: disable= no-member, protected-access return _internal._onehot_encode(indices, out, out=out)
# pylint: enable= no-member, protected-access
[docs]def ones(shape, ctx=None, dtype=None, **kwargs): """Returns a new array filled with all ones, with the given shape and type. Parameters ---------- shape : int or tuple of int or list of int The shape of the empty array. ctx : Context, optional An optional device context. Defaults to the current default context (``mxnet.Context.default_ctx``). dtype : str or numpy.dtype, optional An optional value type (default is `float32`). out : NDArray, optional The output NDArray (default is `None`). Returns ------- NDArray A new array of the specified shape filled with all ones. Examples -------- >>> mx.nd.ones(1).asnumpy() array([ 1.], dtype=float32) >>> mx.nd.ones((1,2), mx.gpu(0)) >>> mx.nd.ones((1,2), dtype='float16').asnumpy() array([[ 1., 1.]], dtype=float16) """ # pylint: disable= unused-argument if ctx is None: ctx = Context.default_ctx dtype = mx_real_t if dtype is None else dtype # pylint: disable= no-member, protected-access return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs)
# pylint: enable= no-member, protected-access
[docs]def full(shape, val, ctx=None, dtype=mx_real_t, out=None): """Returns a new array of given shape and type, filled with the given value `val`. Parameters -------- shape : int or tuple of int The shape of the new array. val : scalar Fill value. ctx : Context, optional Device context (default is the current default context). dtype : `str` or `numpy.dtype`, optional The data type of the returned `NDArray`. The default datatype is `float32`. out : NDArray, optional The output NDArray (default is `None`). Returns ------- NDArray `NDArray` filled with `val`, with the given shape, ctx, and dtype. Examples -------- >>> mx.nd.full(1, 2.0).asnumpy() array([ 2.], dtype=float32) >>> mx.nd.full((1, 2), 2.0, mx.gpu(0)) >>> mx.nd.full((1, 2), 2.0, dtype='float16').asnumpy() array([[ 2., 2.]], dtype=float16) """ out = empty(shape, ctx, dtype) if out is None else out out[:] = val return out
def array(source_array, ctx=None, dtype=None): """Creates an array from any object exposing the array interface. Parameters ---------- source_array : array_like An object exposing the array interface, an object whose `__array__` method returns an array, or any (nested) sequence. ctx : Context, optional Device context (default is the current default context). dtype : str or numpy.dtype, optional The data type of the output array. The default dtype is ``source_array.dtype`` if `source_array` is an `NDArray`, `float32` otherwise. Returns ------- NDArray An `NDArray` with the same contents as the `source_array`. """ if isinstance(source_array, NDArray): dtype = source_array.dtype if dtype is None else dtype else: dtype = mx_real_t if dtype is None else dtype if not isinstance(source_array, np.ndarray): try: source_array = np.array(source_array, dtype=dtype) except: raise TypeError('source_array must be array like object') arr = empty(source_array.shape, ctx, dtype) arr[:] = source_array return arr
[docs]def moveaxis(tensor, source, destination): """Moves the `source` axis into the `destination` position while leaving the other axes in their original order Parameters ---------- tensor : mx.nd.array The array which axes should be reordered source : int Original position of the axes to move. destination : int Destination position for each of the original axes. Returns ------- result : mx.nd.array Array with moved axes. Examples -------- >>> X = mx.nd.array([[1, 2, 3], [4, 5, 6]]) >>> mx.nd.moveaxis(X, 0, 1).shape (3L, 2L) """ axes = list(range(tensor.ndim)) try: axes.pop(source) except IndexError: raise ValueError('Source should verify 0 <= source < tensor.ndim' 'Got %d' % source) try: axes.insert(destination, source) except IndexError: raise ValueError('Destination should verify 0 <= destination < tensor.ndim' 'Got %d' % destination) return op.transpose(tensor, axes)
# pylint: disable= no-member, protected-access, too-many-arguments, redefined-outer-name
[docs]def arange(start, stop=None, step=1.0, repeat=1, ctx=None, dtype=mx_real_t): """Returns evenly spaced values within a given interval. Values are generated within the half-open interval [`start`, `stop`). In other words, the interval includes `start` but excludes `stop`. The function is similar to the built-in Python function `range` and to `numpy.arange`, but returns an `NDArray`. Parameters ---------- start : number, optional Start of interval. The default start value is 0. stop : number End of interval. step : number, optional Spacing between values. The default step size is 1. repeat : int, optional Number of times to repeat each element. The default repeat count is 1. ctx : Context, optional Device context. Default context is the current default context. dtype : str or numpy.dtype, optional The data type of the `NDArray`. The default datatype is `np.float32`. Returns ------- NDArray `NDArray` of evenly spaced values in the specified range. Examples -------- >>> mx.nd.arange(3).asnumpy() array([ 0., 1., 2.], dtype=float32) >>> mx.nd.arange(2, 6).asnumpy() array([ 2., 3., 4., 5.], dtype=float32) >>> mx.nd.arange(2, 6, step=2).asnumpy() array([ 2., 4.], dtype=float32) >>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy() array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32) >>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy() array([2, 2, 2, 4, 4, 4], dtype=int32) """ if ctx is None: ctx = Context.default_ctx return _internal._arange(start=start, stop=stop, step=step, repeat=repeat, dtype=dtype, ctx=str(ctx))
# pylint: enable= no-member, protected-access, too-many-arguments #pylint: disable= too-many-arguments, no-member, protected-access def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None): """ Helper function for element-wise operation. The function will perform numpy-like broadcasting if needed and call different functions. Parameters -------- lhs : NDArray or numeric value Left-hand side operand. rhs : NDArray or numeric value Right-hand operand, fn_array : function 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 rfn_scalar : function 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 -------- NDArray result array """ if isinstance(lhs, numeric_types): if isinstance(rhs, numeric_types): return fn_scalar(lhs, rhs) else: if rfn_scalar is None: # commutative function return lfn_scalar(rhs, float(lhs)) else: return rfn_scalar(rhs, float(lhs)) elif isinstance(rhs, numeric_types): return lfn_scalar(lhs, float(rhs)) elif isinstance(rhs, NDArray): return fn_array(lhs, rhs) else: raise TypeError('type %s not supported' % str(type(rhs))) #pylint: enable= too-many-arguments, no-member, protected-access
[docs]def add(lhs, rhs): """Returns element-wise sum of the input arrays with broadcasting. Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and ``mx.nd.broadcast_plus(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be added. rhs : scalar or array Second array to be added. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise sum of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x+2).asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32) >>> (x+y).asnumpy() array([[ 1., 1., 1.], [ 2., 2., 2.]], dtype=float32) >>> mx.nd.add(x,y).asnumpy() array([[ 1., 1., 1.], [ 2., 2., 2.]], dtype=float32) >>> (z + y).asnumpy() array([[ 0., 1.], [ 1., 2.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_add, operator.add, _internal._plus_scalar, None)
# pylint: enable= no-member, protected-access
[docs]def subtract(lhs, rhs): """Returns element-wise difference of the input arrays with broadcasting. Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and ``mx.nd.broadcast_minus(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be subtracted. rhs : scalar or array Second array to be subtracted. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise difference of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x-2).asnumpy() array([[-1., -1., -1.], [-1., -1., -1.]], dtype=float32) >>> (x-y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.subtract(x,y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> (z-y).asnumpy() array([[ 0., 1.], [-1., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_sub, operator.sub, _internal._minus_scalar, _internal._rminus_scalar)
# pylint: enable= no-member, protected-access
[docs]def multiply(lhs, rhs): """Returns element-wise product of the input arrays with broadcasting. Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be multiplied. rhs : scalar or array Second array to be multiplied. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise multiplication of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x*2).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> (x*y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.multiply(x, y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> (z*y).asnumpy() array([[ 0., 0.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_mul, operator.mul, _internal._mul_scalar, None)
# pylint: enable= no-member, protected-access
[docs]def divide(lhs, rhs): """Returns element-wise division of the input arrays with broadcasting. Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array in division. rhs : scalar or array Second array in division. The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise division of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3))*6 >>> y = mx.nd.ones((2,1))*2 >>> x.asnumpy() array([[ 6., 6., 6.], [ 6., 6., 6.]], dtype=float32) >>> y.asnumpy() array([[ 2.], [ 2.]], dtype=float32) >>> x/2 >>> (x/3).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> (x/y).asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32) >>> mx.nd.divide(x,y).asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_div, operator.truediv, _internal._div_scalar, _internal._rdiv_scalar)
# pylint: enable= no-member, protected-access
[docs]def modulo(lhs, rhs): """Returns element-wise modulo of the input arrays with broadcasting. Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array in modulo. rhs : scalar or array Second array in modulo. The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise modulo of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3))*6 >>> y = mx.nd.ones((2,1))*4 >>> x.asnumpy() array([[ 6., 6., 6.], [ 6., 6., 6.]], dtype=float32) >>> y.asnumpy() array([[ 4.], [ 4.]], dtype=float32) >>> x%5 >>> (x%5).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x%y).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> mx.nd.modulo(x,y).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_mod, operator.mod, _internal._mod_scalar, _internal._rmod_scalar)
# pylint: enable= no-member, protected-access
[docs]def power(base, exp): """Returns result of first array elements raised to powers from second array, element-wise with broadcasting. Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- base : scalar or NDArray The base array exp : scalar or NDArray The exponent array. If ``base.shape != exp.shape``, they must be broadcastable to a common shape. Returns -------- NDArray The bases in x raised to the exponents in y. Examples -------- >>> x = mx.nd.ones((2,3))*2 >>> y = mx.nd.arange(1,3).reshape((2,1)) >>> z = mx.nd.arange(1,3).reshape((2,1)) >>> x.asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> y.asnumpy() array([[ 1.], [ 2.]], dtype=float32) >>> z.asnumpy() array([[ 1.], [ 2.]], dtype=float32) >>> (x**2).asnumpy() array([[ 4., 4., 4.], [ 4., 4., 4.]], dtype=float32) >>> (x**y).asnumpy() array([[ 2., 2., 2.], [ 4., 4., 4.]], dtype=float32) >>> mx.nd.power(x,y).asnumpy() array([[ 2., 2., 2.], [ 4., 4., 4.]], dtype=float32) >>> (z**y).asnumpy() array([[ 1.], [ 4.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( base, exp, op.broadcast_power, operator.pow, _internal._power_scalar, _internal._rpower_scalar)
# pylint: enable= no-member, protected-access
[docs]def maximum(lhs, rhs): """Returns element-wise maximum of the input arrays with broadcasting. Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise maximum of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> mx.nd.maximum(x, 2).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> mx.nd.maximum(x, y).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.maximum(y, z).asnumpy() array([[ 0., 1.], [ 1., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_maximum, lambda x, y: x if x > y else y, _internal._maximum_scalar, None)
# pylint: enable= no-member, protected-access
[docs]def minimum(lhs, rhs): """Returns element-wise minimum of the input arrays with broadcasting. Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise minimum of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> mx.nd.minimum(x, 2).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.minimum(x, y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.minimum(z, y).asnumpy() array([[ 0., 0.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_minimum, lambda x, y: x if x < y else y, _internal._minimum_scalar, None)
# pylint: enable= no-member, protected-access
[docs]def equal(lhs, rhs): """Returns the result of element-wise **equal to** (==) comparison operation with broadcasting. For each element in input arrays, return 1(true) if corresponding elements are same, otherwise return 0(false). Equivalent to ``lhs == rhs`` and ``mx.nd.broadcast_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x == 1).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x == y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.equal(x,y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> (z == y).asnumpy() array([[ 1., 0.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_equal, lambda x, y: 1 if x == y else 0, _internal._equal_scalar, None)
# pylint: enable= no-member, protected-access
[docs]def not_equal(lhs, rhs): """Returns the result of element-wise **not equal to** (!=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if corresponding elements are different, otherwise return 0(false). Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (z == y).asnumpy() array([[ 1., 0.], [ 0., 1.]], dtype=float32) >>> (x != 1).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (x != y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.not_equal(x, y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> (z != y).asnumpy() array([[ 0., 1.], [ 1., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_not_equal, lambda x, y: 1 if x != y else 0, _internal._not_equal_scalar, None)
# pylint: enable= no-member, protected-access
[docs]def greater(lhs, rhs): """Returns the result of element-wise **greater than** (>) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are greater than rhs, otherwise return 0(false). Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x > 1).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (x > y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.greater(x, y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> (z > y).asnumpy() array([[ 0., 1.], [ 0., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_greater, lambda x, y: 1 if x > y else 0, _internal._greater_scalar, _internal._lesser_scalar)
# pylint: enable= no-member, protected-access
[docs]def greater_equal(lhs, rhs): """Returns the result of element-wise **greater than or equal to** (>=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs, otherwise return 0(false). Equivalent to ``lhs >= rhs`` and ``mx.nd.broadcast_greater_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x >= 1).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x >= y).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.greater_equal(x, y).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (z >= y).asnumpy() array([[ 1., 1.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_greater_equal, lambda x, y: 1 if x >= y else 0, _internal._greater_equal_scalar, _internal._lesser_equal_scalar)
# pylint: enable= no-member, protected-access
[docs]def lesser(lhs, rhs): """Returns the result of element-wise **lesser than** (<) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are less than rhs, otherwise return 0(false). Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x < 1).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (x < y).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.lesser(x, y).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (z < y).asnumpy() array([[ 0., 0.], [ 1., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_lesser, lambda x, y: 1 if x < y else 0, _internal._lesser_scalar, _internal._greater_scalar)
# pylint: enable= no-member, protected-access
[docs]def lesser_equal(lhs, rhs): """Returns the result of element-wise **lesser than or equal to** (<=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are lesser than equal to rhs, otherwise return 0(false). Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x <= 1).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x <= y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.lesser_equal(x, y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> (z <= y).asnumpy() array([[ 1., 0.], [ 1., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_lesser_equal, lambda x, y: 1 if x <= y else 0, _internal._lesser_equal_scalar, _internal._greater_equal_scalar)
# pylint: enable= no-member, protected-access
[docs]def true_divide(lhs, rhs): """This function is similar to :meth:`divide`. """ return divide(lhs, rhs)
[docs]def concatenate(arrays, axis=0, always_copy=True): """DEPRECATED, use ``concat`` instead Parameters ---------- arrays : list of `NDArray` Arrays to be concatenate. They must have identical shape except the first dimension. They also must have the same data type. axis : int The axis along which to concatenate. always_copy : bool Default `True`. When not `True`, if the arrays only contain one `NDArray`, that element will be returned directly, avoid copying. Returns ------- NDArray An `NDArray` that lives on the same context as `arrays[0].context`. """ assert isinstance(arrays, list) assert len(arrays) > 0 assert isinstance(arrays[0], NDArray) if not always_copy and len(arrays) == 1: return arrays[0] shape_axis = arrays[0].shape[axis] shape_rest1 = arrays[0].shape[0:axis] shape_rest2 = arrays[0].shape[axis+1:] dtype = arrays[0].dtype for arr in arrays[1:]: shape_axis += arr.shape[axis] assert shape_rest1 == arr.shape[0:axis] assert shape_rest2 == arr.shape[axis+1:] assert dtype == arr.dtype ret_shape = shape_rest1 + (shape_axis,) + shape_rest2 ret = empty(ret_shape, ctx=arrays[0].context, dtype=dtype) idx = 0 begin = [0 for _ in ret_shape] end = list(ret_shape) for arr in arrays: if axis == 0: ret[idx:idx+arr.shape[0]] = arr else: begin[axis] = idx end[axis] = idx+arr.shape[axis] # pylint: disable=no-member,protected-access _internal._crop_assign(ret, arr, out=ret, begin=tuple(begin), end=tuple(end)) # pylint: enable=no-member,protected-access idx += arr.shape[axis] return ret
# pylint: disable=redefined-outer-name
[docs]def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None): """DEPRECATED, use mx.img instead Parameters ---------- str_img : str Binary image data clip_rect : iterable of 4 int Clip decoded image to rectangle (x0, y0, x1, y1). out : NDArray Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w). index : int Output decoded image to i-th slice of 4 dimensional buffer. channels : int Number of channels to output. Decode to grey scale when channels = 1. mean : NDArray Subtract mean from decode image before outputing. """ # pylint: disable= no-member, protected-access, too-many-arguments if mean is None: mean = NDArray(_new_empty_handle()) if out is None: return _internal._imdecode(mean, index, clip_rect[0], clip_rect[1], clip_rect[2], clip_rect[3], channels, len(str_img), str_img=str_img) else: return _internal._imdecode(mean, index, clip_rect[0], clip_rect[1], clip_rect[2], clip_rect[3], channels, len(str_img), str_img=str_img, out=out)
def zeros(shape, ctx=None, dtype=None, **kwargs): """Returns a new array filled with all zeros, with the given shape and type. Parameters ---------- shape : int or tuple of int The shape of the empty array. ctx : Context, optional An optional device context (default is the current default context). dtype : str or numpy.dtype, optional An optional value type (default is `float32`). out : NDArray, optional The output NDArray (default is `None`). Returns ------- NDArray A created array Examples -------- >>> mx.nd.zeros(1).asnumpy() array([ 0.], dtype=float32) >>> mx.nd.zeros((1,2), mx.gpu(0)) >>> mx.nd.zeros((1,2), mx.gpu(0), 'float16').asnumpy() array([[ 0., 0.]], dtype=float16) """ # pylint: disable= unused-argument if ctx is None: ctx = Context.default_ctx dtype = mx_real_t if dtype is None else dtype # pylint: disable= no-member, protected-access return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, **kwargs) # pylint: enable= no-member, protected-access def empty(shape, ctx=None, dtype=None): """Returns a new array of given shape and type, without initializing entries. Parameters ---------- shape : int or tuple of int The shape of the empty array. ctx : Context, optional An optional device context (default is the current default context). dtype : str or numpy.dtype, optional An optional value type (default is `float32`). Returns ------- NDArray A created array. """ if isinstance(shape, int): shape = (shape, ) if ctx is None: ctx = Context.default_ctx if dtype is None: dtype = mx_real_t return NDArray(handle=_new_alloc_handle(shape, ctx, False, dtype))