def corr(self):
'''The correlation matrix'''
cov = self.cov()
N = cov.shape[0]
corr = ndarray((N,N))
for r in range(N):
for c in range(r):
corr[r,c] = corr[c,r] = cov[r,c]/sqrt(cov[r,r]*cov[c,c])
corr[r,r] = 1.
return corr
def corr(self):
'''The correlation matrix'''
cov = self.cov()
N = cov.shape[0]
corr = ndarray((N, N))
for r in range(N):
for c in range(r):
corr[r, c] = corr[c,
r] = cov[r, c] / sqrt(cov[r, r] * cov[c, c])
corr[r, r] = 1.
return corr
def vector_to_symmetric(v):
'''Convert an iterable into a symmetric matrix.'''
np = len(v)
N = (int(sqrt(1 + 8 * np)) - 1) // 2
if N * (N + 1) // 2 != np:
raise ValueError('Cannot convert vector to symmetric matrix')
sym = ndarray((N, N))
iterable = iter(v)
for r in range(N):
for c in range(r + 1):
sym[r, c] = sym[c, r] = iterable.next()
return sym
def vector_to_symmetric(v):
'''Convert an iterable into a symmetric matrix.'''
np = len(v)
N = (int(sqrt(1 + 8*np)) - 1)//2
if N*(N+1)//2 != np:
raise ValueError('Cannot convert vector to symmetric matrix')
sym = ndarray((N,N))
iterable = iter(v)
for r in range(N):
for c in range(r+1):
sym[r,c] = sym[c,r] = iterable.next()
return sym
def testVar(self):
'''Calculate the biased variance of a series'''
ts = dynts.timeseries(date=datepopulate(10),
data=range(1, 11),
backend=self.backend)
self.assertAlmostEqual(ts.var()[0], 8.25, places)
self.assertAlmostEqual(ts.var(ddof=1)[0], 9.166667, places)
def testVar(self):
'''Calculate the biased variance of a series'''
ts = timeseries(date = datepopulate(10), data = range(1,11), backend = self.backend)
self.assertAlmostEqual(ts.var()[0],8.25)
self.assertAlmostEqual(ts.var(ddof=1)[0],9.166667)