generic.conjugate() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

RandomState.standard_gamma(shape, size=None) Draw samples from a standard Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated ?k?) and scale=1. Parameters: shape : float Parameter, should be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. Returns: samples : ndarray or scalar

ndarray.getfield(dtype, offset=0) Returns a field of the given array as a certain type. A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes. Parameters

numpy.logaddexp2(x1, x2[, out]) = Logarithm of the sum of exponentiations of the inputs in base-2. Calculates log2(2**x1 + 2**x2). This function is useful in machine learning when the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases the base-2 logarithm of the calculated probability can be used instead. This function allows adding probabilities stored in such a fashion. Parameters: x1, x2 : array_like Input values.

numpy.reciprocal(x[, out]) = Return the reciprocal of the argument, element-wise. Calculates 1/x. Parameters: x : array_like Input array. Returns: y : ndarray Return array. Notes Note This function is not designed to work with integers. For integer arguments with absolute value larger than 1 the result is always zero because of the way Python handles integer division. For integer zero the result is an overflow. Examples >>> np.reciprocal(2.) 0.5 >>> np.reciproca

numpy.zeros_like(a, dtype=None, order='K', subok=True) [source] Return an array of zeros with the same shape and type as a given array. Parameters: a : array_like The shape and data-type of a define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. New in version 1.6.0. order : {?C?, ?F?, ?A?, or ?K?}, optional Overrides the memory layout of the result. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran c

matrix.nonzero() Return the indices of the elements that are non-zero. Refer to numpy.nonzero for full documentation. See also numpy.nonzero equivalent function

numpy.polynomial.hermite.hermval3d(x, y, z, c) [source] Evaluate a 3-D Hermite series at points (x, y, z). This function returns the values: The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than 3 dimensi

numpy.arccosh(x[, out]) = Inverse hyperbolic cosine, element-wise. Parameters: x : array_like Input array. out : ndarray, optional Array of the same shape as x, to store results in. See doc.ufuncs (Section ?Output arguments?) for details. Returns: arccosh : ndarray Array of the same shape as x. See also cosh, arcsinh, sinh, arctanh, tanh Notes arccosh is a multivalued function: for each x there are infinitely many numbers z such that cosh(z) = x. The convention is to return the

matrix.dtype Data-type of the array?s elements. Parameters: None Returns: d : numpy dtype object See also numpy.dtype Examples >>> x array([[0, 1], [2, 3]]) >>> x.dtype dtype('int32') >>> type(x.dtype) <type 'numpy.dtype'>