101 lines
2.7 KiB
Python
101 lines
2.7 KiB
Python
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from numpy import *
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HAS_SYMPOWER=True
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try:
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from _numpy_module import sym_powerit
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except:
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raise ImportError("Sym_powerit module not present")
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HAS_SYMPOWER = False
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class SymPowerException(Exception):
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pass
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_ERRORCODES = {1: "Some eigenvectors did not converge, try to increase \nthe number of iterations or lower the tolerance level",
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0: ""}
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def sympowerit(xx, T0=None, mn_center=False, a_max=10, tol=1e-7, maxiter=100,
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verbose=0):
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"""Estimate eigenvectos of a symmetric matrix using the power method.
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*Parameters*:
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xx : {array}
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Symmetric square array (m, m)
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T0 : {array}
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Initial solution (m, a_max), optional
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mn_center : {boolean}, optional
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Mean centering
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a_max : {integer}, optional
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Number of components to extract
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tol : {float}, optional
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Tolerance level of eigenvector solver
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maxiter : {integer}
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Maximum number of poweriterations to use
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verbose : {integer}
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Debug output (==1)
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*Returns*:
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v : {array}
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Eigenvectors of xx, (m , a_max)
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"""
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valid_types = ['D','d','F','f']
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dtype = xx.dtype.char
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n, m = xx.shape
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if not(dtype in valid_types):
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msg = "Array type: (%s) needs to be a float or double" %dtype
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raise SymPowerException(msg)
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if not (m==n):
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msg = "Input array needs to be square, input: (%d,%d)" %(m,n)
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raise SymPowerException(msg)
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# small test of symmetry
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N = 5
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num = random.randint(0,n,N)
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for i in range(5):
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j = N-5
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if abs(xx[num[i],num[j]] - xx[num[j],num[i]])>1e-15:
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msg = "Array needs to be symmetric"
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raise SymPowerException(msg)
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if not a_max:
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a_max = 10
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if T0 !=None:
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tn, tm = T0.shape
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if not (tn==n):
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msg = "Start eigenvectors need to match input array ()"
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raise SymPowerException(msg)
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if not (tm==a_max):
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msg = "Start eigenvectors need to match input a_max ()"
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raise SymPowerException(msg)
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else:
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T0 = zeros((n, a_max), 'd')
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T0[0,:] = ones((a_max,),'d')
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if mn_center:
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xx = _center(xx)
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# call c-function
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T, info = sym_powerit(xx, T0, n, a_max, tol, maxiter, verbose)
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if info != 0:
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if verbose:
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print _ERRORCODES.get(info, "Dont know this error")
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return T
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def _center(xx, ret_mn=False):
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"""Returns mean centered symmetric kernel matrix.
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"""
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n = xx.shape[0]
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h = xx.sum(0)[:,newaxis]
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h = (h - mean(h)/2)/n
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mn_a = h + h.T
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xxc = xx - mn_a
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if ret_mn:
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return xxc, mn_a
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return xxc
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