Modules
		copy copy_reg math numpy.numarray.numerictypes operator os numpy.numarray.session sys types

Functions
		innerproduct = inner(...) inner(a, b)   Inner product of two arrays.   Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.   Parameters ---------- a, b : array_like     If `a` and `b` are nonscalar, their last dimensions of must match.   Returns ------- out : ndarray     `out.shape = a.shape[:-1] + b.shape[:-1]`   Raises ------ ValueError     If the last dimension of `a` and `b` has different size.   See Also -------- tensordot : Sum products over arbitrary axes. dot : Generalised matrix product, using second last dimension of `b`.   Notes ----- For vectors (1-D arrays) it computes the ordinary inner-product::       np.inner(a, b) = sum(a[:]*b[:])   More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::       np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))   or explicitly::       np.inner(a, b)[i0,...,ir-1,j0,...,js-1]          = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])   In addition `a` or `b` may be scalars, in which case::      np.inner(a,b) = a*b   Examples -------- Ordinary inner product for vectors:   >>> a = np.array([1,2,3]) >>> b = np.array([0,1,0]) >>> np.inner(a, b) 2   A multidimensional example:   >>> a = np.arange(24).reshape((2,3,4)) >>> b = np.arange(4) >>> np.inner(a, b) array([[ 14,  38,  62],        [ 86, 110, 134]])   An example where `b` is a scalar:   >>> np.inner(np.eye(2), 7) array([[ 7.,  0.],        [ 0.,  7.]]) matrixmultiply = dot(...) dot(a, b)   Dot product of two arrays.   For 2-D arrays it is equivalent to matrix multiplication, and for 1-D arrays to inner product of vectors (without complex conjugation). For N dimensions it is a sum product over the last axis of `a` and the second-to-last of `b`::       dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])   Parameters ---------- a : array_like     First argument. b : array_like     Second argument.   Returns ------- output : ndarray     Returns the dot product of `a` and `b`.  If `a` and `b` are both     scalars or both 1-D arrays then a scalar is returned; otherwise     an array is returned.   Raises ------ ValueError     If the last dimension of `a` is not the same size as     the second-to-last dimension of `b`.   See Also -------- vdot : Complex-conjugating dot product. tensordot : Sum products over arbitrary axes.   Examples -------- >>> np.dot(3, 4) 12   Neither argument is complex-conjugated:   >>> np.dot([2j, 3j], [2j, 3j]) (-13+0j)   For 2-D arrays it's the matrix product:   >>> a = [[1, 0], [0, 1]] >>> b = [[4, 1], [2, 2]] >>> np.dot(a, b) array([[4, 1],        [2, 2]])   >>> a = np.arange(3*4*5*6).reshape((3,4,5,6)) >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3)) >>> np.dot(a, b)[2,3,2,1,2,2] 499128 >>> sum(a[2,3,2,:] * b[1,2,:,2]) 499128

Data
		Any = <numpy.numarray.numerictypes.AnyType object at 0x4f81b90> Bool = <numpy.numarray.numerictypes.BooleanType object at 0x4f81c10> Byte = <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81c50> Complex = <numpy.numarray.numerictypes.ComplexType object at 0x4f81f10> Complex32 = <numpy.numarray.numerictypes.ComplexType object at 0x4f81ed0> Complex64 = <numpy.numarray.numerictypes.ComplexType object at 0x4f81f10> Float = <numpy.numarray.numerictypes.FloatingType object at 0x4f81d90> Float32 = <numpy.numarray.numerictypes.FloatingType object at 0x4f81d50> Float64 = <numpy.numarray.numerictypes.FloatingType object at 0x4f81d90> HasUInt64 = 1 Int = <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81cd0> Int16 = <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81c90> Int32 = <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81cd0> Int64 = <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81d10> Int8 = <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81c50> Long = <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81d10> MaybeLong = <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81d10> NewAxis = None Object = <numpy.numarray.numerictypes.ObjectType object at 0x4f81bd0> SLOPPY = 1 STRICT = 0 Short = <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81c90> UInt16 = <numpy.numarray.numerictypes.UnsignedIntegralType object at 0x4f81e10> UInt32 = <numpy.numarray.numerictypes.UnsignedIntegralType object at 0x4f81e50> UInt64 = <numpy.numarray.numerictypes.UnsignedIntegralType object at 0x4f81e90> UInt8 = <numpy.numarray.numerictypes.UnsignedIntegralType object at 0x4f81dd0> WARN = 2 __all__ = ['session', 'numerictypes', 'MathDomainError', 'UnderflowError', 'NumOverflowError', 'handleError', 'get_numarray_include_dirs', 'NumericType', 'HasUInt64', 'typeDict', 'IsType', 'BooleanType', 'SignedType', 'UnsignedType', 'IntegralType', 'SignedIntegralType', 'UnsignedIntegralType', 'FloatingType', 'ComplexType', 'AnyType', ...] __file__ = '/usr/lib/python2.6/dist-packages/numpy/numarray/__init__.pyc' __name__ = 'numpy.numarray.__init__' __package__ = 'numpy.numarray' genericCoercions = {('Bool', 'Bool'): 'Bool', ('Bool', 'Complex32'): 'Complex32', ('Bool', 'Complex64'): 'Complex64', ('Bool', 'Float32'): 'Float32', ('Bool', 'Float64'): 'Float64', ('Bool', 'Int16'): 'Int16', ('Bool', 'Int32'): 'Int32', ('Bool', 'Int64'): 'Int64', ('Bool', 'Int8'): 'Int8', ('Bool', 'Object'): 'Object', ...} genericPromotionExclusions = {'Bool': (), 'Complex32': (), 'Complex64': (), 'Float32': (), 'Float64': ('Complex32',), 'Int16': (), 'Int32': ('Float32', 'Complex32'), 'Int64': ('Float32', 'Complex32'), 'Int8': (), 'UInt16': (), ...} genericTypeRank = ['Bool', 'Int8', 'UInt8', 'Int16', 'UInt16', 'Int32', 'UInt32', 'Int64', 'UInt64', 'Float32', 'Float64', 'Complex32', 'Complex64', 'Object'] isBigEndian = False lshift = <ufunc 'left_shift'> minus = <ufunc 'negative'> pythonTypeMap = {<type 'float'>: ('Float64', 'float'), <type 'int'>: ('Int64', 'int'), <type 'long'>: ('Int64', 'int'), <type 'bool'>: ('Bool', 'bool'), <type 'complex'>: ('Complex64', 'complex')} pythonTypeRank = [<type 'bool'>, <type 'int'>, <type 'long'>, <type 'float'>, <type 'complex'>] rshift = <ufunc 'right_shift'> scalarTypeMap = {<type 'float'>: 'Float64', <type 'int'>: 'Int64', <type 'long'>: 'Int64', <type 'bool'>: 'Bool', <type 'complex'>: 'Complex64'} scalarTypes = (<type 'bool'>, <type 'int'>, <type 'long'>, <type 'float'>, <type 'complex'>) tcode = 'f' tname = 'Float32' typeDict = {'1': <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81c50>, 'Any': <numpy.numarray.numerictypes.AnyType object at 0x4f81b90>, 'B': <numpy.numarray.numerictypes.BooleanType object at 0x4f81c10>, 'Bool': <numpy.numarray.numerictypes.BooleanType object at 0x4f81c10>, 'Byte': <numpy.numarray.numerictypes.SignedIntegralType object at 0x4f81c50>, 'Complex': <numpy.numarray.numerictypes.ComplexType object at 0x4f81f10>, 'Complex32': <numpy.numarray.numerictypes.ComplexType object at 0x4f81ed0>, 'Complex64': <numpy.numarray.numerictypes.ComplexType object at 0x4f81f10>, 'D': <numpy.numarray.numerictypes.ComplexType object at 0x4f81f10>, 'F': <numpy.numarray.numerictypes.ComplexType object at 0x4f81ed0>, ...} typecodes = {'Character': 'c', 'Complex': 'FD', 'Float': 'fd', 'Integer': '1silN', 'UnsignedInteger': 'bBwuU'} value = 'f'