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Per.Andreas.Brodtkorb 13 years ago
parent 3257d675d8
commit a7a00851cc
1 changed files with 32 additions and 24 deletions
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pywafo/src/wafo/kdetools.py
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@ -159,7 +159,7 @@ class _KDE(object):
self.args = args self.args = args
return self._eval_grid_fun(self._eval_grid_fast, *args, **kwds) return self._eval_grid_fun(self._eval_grid_fast, *args, **kwds)
def _eval_grid_fast(self, *args): def _eval_grid_fast(self, *args, **kwds):
pass pass
def eval_grid(self, *args, **kwds): def eval_grid(self, *args, **kwds):
@ -189,7 +189,7 @@ class _KDE(object):
def _eval_grid(self, *args): def _eval_grid(self, *args):
pass pass
def _eval_grid_fun(self, eval_grd, *args, **kwds): def _eval_grid_fun(self, eval_grd, *args, **kwds):
f = eval_grd(*args) f = eval_grd(*args, **kwds)
if kwds.get('output', 'value') == 'value': if kwds.get('output', 'value') == 'value':
return f return f
else: else:
@ -223,7 +223,7 @@ class _KDE(object):
self.d) self.d)
raise ValueError(msg) raise ValueError(msg)
return points return points
def eval_points(self, points): def eval_points(self, points, r=0):
"""Evaluate the estimated pdf on a set of points. """Evaluate the estimated pdf on a set of points.
Parameters Parameters
@ -244,9 +244,9 @@ class _KDE(object):
""" """
points = self._check_shape(points) points = self._check_shape(points)
return self._eval_points(points) return self._eval_points(points, r=r)
def _eval_points(self, points): def _eval_points(self, points, r=0):
pass pass
__call__ = eval_grid __call__ = eval_grid
@ -624,7 +624,7 @@ class KDE(_KDE):
self._norm_factor = deth * self.n self._norm_factor = deth * self.n
def _eval_grid_fast(self, *args): def _eval_grid_fast(self, *args, **kwds):
X = np.vstack(args) X = np.vstack(args)
d, inc = X.shape d, inc = X.shape
dx = X[:, 1] - X[:, 0] dx = X[:, 1] - X[:, 0]
@ -645,33 +645,38 @@ class KDE(_KDE):
Xn = np.dot(self.inv_hs, np.vstack(Xnc)) Xn = np.dot(self.inv_hs, np.vstack(Xnc))
# Obtain the kernel weights. # Obtain the kernel weights.
kw = self.kernel(Xn) / (self._norm_factor * self.kernel.norm_factor(d, self.n)) r = kwds.get('r', 0)
if r==0:
kw = self.kernel(Xn) / (self._norm_factor * self.kernel.norm_factor(d, self.n))
else:
kw = np.vstack(Xnc) ** r * self.kernel(Xn) / (self._norm_factor * self.kernel.norm_factor(d, self.n))
kw.shape = shape0 kw.shape = shape0
kw = np.fft.ifftshift(kw) kw = np.fft.ifftshift(kw)
fftn = np.fft.fftn fftn = np.fft.fftn
ifftn = np.fft.ifftn ifftn = np.fft.ifftn
y = kwds.get('y', 1)
# Find the binned kernel weights, c. # Find the binned kernel weights, c.
c = gridcount(self.dataset, X) c = gridcount(self.dataset, X, y=y)
# Perform the convolution. # Perform the convolution.
z = np.real(ifftn(fftn(c, s=nfft) * fftn(kw))) z = np.real(ifftn(fftn(c, s=nfft) * fftn(kw)))
ix = (slice(0, inc),)*d ix = (slice(0, inc),)*d
return z[ix] * (z[ix] > 0.0) return z[ix] * (z[ix] > 0.0)
def _eval_grid(self, *args): def _eval_grid(self, *args, **kwds):
grd = meshgrid(*args) if len(args) > 1 else list(args) grd = meshgrid(*args) if len(args) > 1 else list(args)
shape0 = grd[0].shape shape0 = grd[0].shape
d = len(grd) d = len(grd)
for i in range(d): for i in range(d):
grd[i] = grd[i].ravel() grd[i] = grd[i].ravel()
f = self.eval_points(np.vstack(grd)) r = kwds.get('r', 0)
f = self.eval_points(np.vstack(grd), r=r)
return f.reshape(shape0) return f.reshape(shape0)
def _eval_points(self, points): def _eval_points(self, points, r=0):
"""Evaluate the estimated pdf on a set of points. """Evaluate the estimated pdf on a set of points.
Parameters Parameters
@ -693,19 +698,23 @@ class KDE(_KDE):
d, m = points.shape d, m = points.shape
result = np.zeros((m,)) result = np.zeros((m,))
if r==0:
fun = lambda xi : 1
else:
fun = lambda xi : xi ** r
if m >= self.n: if m >= self.n:
# there are more points than data, so loop over data # there are more points than data, so loop over data
for i in range(self.n): for i in range(self.n):
diff = self.dataset[:, i, np.newaxis] - points diff = self.dataset[:, i, np.newaxis] - points
tdiff = np.dot(self.inv_hs / self._lambda[i], diff) tdiff = np.dot(self.inv_hs / self._lambda[i], diff)
result += self.kernel(tdiff) / self._lambda[i] ** d result += fun(diff) * self.kernel(tdiff) / self._lambda[i] ** d
else: else:
# loop over points # loop over points
for i in range(m): for i in range(m):
diff = self.dataset - points[:, i, np.newaxis] diff = self.dataset - points[:, i, np.newaxis]
tdiff = np.dot(self.inv_hs, diff / self._lambda[np.newaxis, :]) tdiff = np.dot(self.inv_hs, diff / self._lambda[np.newaxis, :])
tmp = self.kernel(tdiff) / self._lambda ** d tmp = fun(diff) * self.kernel(tdiff) / self._lambda ** d
result[i] = tmp.sum(axis= -1) result[i] = tmp.sum(axis= -1)
result /= (self._norm_factor * self.kernel.norm_factor(d, self.n)) result /= (self._norm_factor * self.kernel.norm_factor(d, self.n))
@ -2194,7 +2203,7 @@ def bitget(int_type, offset):
return np.bitwise_and(int_type, 1 << offset) >> offset return np.bitwise_and(int_type, 1 << offset) >> offset
def gridcount(data, X, use_sparse=False): def gridcount(data, X, y=1):
''' '''
Returns D-dimensional histogram using linear binning. Returns D-dimensional histogram using linear binning.
@ -2253,6 +2262,7 @@ def gridcount(data, X, use_sparse=False):
''' '''
dat = np.atleast_2d(data) dat = np.atleast_2d(data)
x = np.atleast_2d(X) x = np.atleast_2d(X)
y = np.atleast_1d(y).ravel()
d = dat.shape[0] d = dat.shape[0]
d1, inc = x.shape d1, inc = x.shape
@ -2269,6 +2279,7 @@ def gridcount(data, X, use_sparse=False):
raise ValueError('X does not include whole range of the data!') raise ValueError('X does not include whole range of the data!')
csiz = np.repeat(inc, d) csiz = np.repeat(inc, d)
use_sparse = False
if use_sparse: if use_sparse:
acfun = accumsum # faster than accum acfun = accumsum # faster than accum
else: else:
@ -2279,8 +2290,9 @@ def gridcount(data, X, use_sparse=False):
abs = np.abs abs = np.abs
if d == 1: if d == 1:
x.shape = (-1,) x.shape = (-1,)
c = np.asarray((acfun(binx, (x[binx + 1] - dat), size=(inc, )) + c = np.asarray((acfun(binx, (x[binx + 1] - dat) * y, size=(inc, )) +
acfun(binx+1, (dat - x[binx]), size=(inc, ))) / w).ravel() acfun(binx+1, (dat - x[binx]) * y, size=(inc, ))) / w).ravel()
# elif d == 2: # elif d == 2:
# b2 = binx[1] # b2 = binx[1]
# b1 = binx[0] # b1 = binx[0]
@ -2294,9 +2306,6 @@ def gridcount(data, X, use_sparse=False):
else: # % d>2 else: # % d>2
Nc = csiz.prod() Nc = csiz.prod()
# if use_sparse:
# c = sparse.csr_matrix((Nc,1))
# else:
c = np.zeros((Nc,)) c = np.zeros((Nc,))
fact2 = np.asarray(np.reshape(inc * np.arange(d), (d, -1)), dtype=int) fact2 = np.asarray(np.reshape(inc * np.arange(d), (d, -1)), dtype=int)
@ -2314,9 +2323,8 @@ def gridcount(data, X, use_sparse=False):
b1 = np.sum((binx + bt0[one]) * fact1, axis=0) #linear index to c b1 = np.sum((binx + bt0[one]) * fact1, axis=0) #linear index to c
bt2 = bt0[two] + fact2 bt2 = bt0[two] + fact2
b2 = binx + bt2 # linear index to X b2 = binx + bt2 # linear index to X
c += acfun(b1, abs(np.prod(X1[b2] - dat, axis=0)), size=(Nc,)) c += acfun(b1, abs(np.prod(X1[b2] - dat, axis=0)) * y, size=(Nc,))
# if use_sparse:
# c = c.toarray().ravel()
c = np.reshape(c/w , csiz, order='F') c = np.reshape(c/w , csiz, order='F')
T = range(d) T = range(d)

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