Exemple #1
0
 def is_chronological(self):
     """
 Returns whether the dates are sorted in chronological order
     """
     _cached = self._cachedinfo
     chronoflag = _cached['ischrono']
     if chronoflag is None:
         sortidx = ndarray.argsort(self.__array__(), axis=None)
         chronoflag = (sortidx == np.arange(self.size)).all()
         _cached['ischrono'] = chronoflag
         if chronoflag:
             _cached['chronidx'] = np.array([], dtype=int)
         else:
             _cached['chronidx'] = sortidx
     return chronoflag
Exemple #2
0
 def is_chronological(self):
     """
 Returns whether the dates are sorted in chronological order
     """
     _cached = self._cachedinfo
     chronoflag = _cached['ischrono']
     if chronoflag is None:
         sortidx = ndarray.argsort(self.__array__(), axis=None)
         chronoflag = (sortidx == np.arange(self.size)).all()
         _cached['ischrono'] = chronoflag
         if chronoflag:
             _cached['chronidx'] = np.array([], dtype=int)
         else:
             _cached['chronidx'] = sortidx
     return chronoflag
Exemple #3
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	for i in xrange(0,len(vecRawStats)):
		vecVar[k] += vecRawStats[i][k]*vecRawStats[i][k];
	vecVar[k] /= float(len(vecRawStats));
	vecVar[k] = math.sqrt(vecVar[k]);

for i in xrange(0,len(vecRawStats)):
	for j in xrange(0,vecDim):
		vecRawStats[i][j] = vecRawStats[i][j]/vecVar[j];

for i in xrange(0,len(vecRawStats)):
	for j in xrange(0,vecDim):
		for k in xrange(0,vecDim):
			scatMatrix[j][k] += vecRawStats[i][j] * vecRawStats[i][j];

w,v = linalg.eigh(scatMatrix);
wArgSorted = ndarray.argsort(w);

transMatrix = [v[:,wArgSorted[vecDim-1]],v[:,wArgSorted[vecDim-2]]]
print transMatrix

analyzedData = [[0 for col in range(2)] for row in range(len(vecRawStats))]
xi = [];
yi = [];
xj = [];
yj = [];
xk = [];
yk = [];
xa = [];
ya = [];

for i in xrange(0,len(vecRawStats)):
Exemple #4
0
    for i in xrange(0, len(vecRawStats)):
        vecVar[k] += vecRawStats[i][k] * vecRawStats[i][k]
    vecVar[k] /= float(len(vecRawStats))
    vecVar[k] = math.sqrt(vecVar[k])

for i in xrange(0, len(vecRawStats)):
    for j in xrange(0, vecDim):
        vecRawStats[i][j] = vecRawStats[i][j] / vecVar[j]

for i in xrange(0, len(vecRawStats)):
    for j in xrange(0, vecDim):
        for k in xrange(0, vecDim):
            scatMatrix[j][k] += vecRawStats[i][j] * vecRawStats[i][j]

w, v = linalg.eigh(scatMatrix)
wArgSorted = ndarray.argsort(w)

transMatrix = [v[:, wArgSorted[vecDim - 1]], v[:, wArgSorted[vecDim - 2]]]
print transMatrix

analyzedData = [[0 for col in range(2)] for row in range(len(vecRawStats))]
xi = []
yi = []
xj = []
yj = []
xk = []
yk = []
xa = []
ya = []

for i in xrange(0, len(vecRawStats)):