def _predict_values_sparse(self, x, n):
if is_classification_problem(self.svm_type):
kvalue = N.empty((len(self.support_vectors),))
for i, sv in enumerate(self.support_vectors):
kvalue[i] = svm_node_dot(x, sv, self.kernel)
p = 0
dec_values = N.empty((n,))
for i in range(self.nr_class):
for j in range(i + 1, self.nr_class):
sum = 0
si, sj = self.start[i], self.start[j]
ci, cj = self.nSV[i], self.nSV[j]
coef1 = self.sv_coef[j - 1]
coef2 = self.sv_coef[i]
sum = 0.
for k in range(ci):
sum += coef1[si + k] * kvalue[si + k]
for k in range(cj):
sum += coef2[sj + k] * kvalue[sj + k]
dec_values[p] = sum - self.rho[p]
p += 1
return dec_values
else:
z = -self.rho
for sv_coef, sv in izip(self.sv_coef, self.support_vectors):
z += sv_coef * svm_node_dot(x, sv, self.kernel)
return z
def _predict_values_sparse(self, x, n):
if is_classification_problem(self.svm_type):
kvalue = N.empty((len(self.support_vectors),))
for i, sv in enumerate(self.support_vectors):
kvalue[i] = svm_node_dot(x, sv, self.kernel)
p = 0
dec_values = N.empty((n,))
for i in range(self.nr_class):
for j in range(i + 1, self.nr_class):
sum = 0
si, sj = self.start[i], self.start[j]
ci, cj = self.nSV[i], self.nSV[j]
coef1 = self.sv_coef[j - 1]
coef2 = self.sv_coef[i]
sum = 0.
for k in range(ci):
sum += coef1[si + k] * kvalue[si + k]
for k in range(cj):
sum += coef2[sj + k] * kvalue[sj + k]
dec_values[p] = sum - self.rho[p]
p += 1
return dec_values
else:
z = -self.rho
for sv_coef, sv in izip(self.sv_coef, self.support_vectors):
z += sv_coef * svm_node_dot(x, sv, self.kernel)
return z
def _predict_values_compact(self, x, n):
if is_classification_problem(self.svm_type):
for i, (sv, kernel) in \
enumerate(izip(self.support_vectors, self.kernels)):
kvalue = N.empty((len(self.support_vectors),))
kvalue[i] = svm_node_dot(x, sv, kernel)
kvalue -= self.rho
return kvalue
else:
sv = self.support_vectors[0]
kernel = self.kernels[0]
kvalue = svm_node_dot(x, sv, kernel) - self.rho
return kvalue
def _predict_values_compact(self, x, n):
if is_classification_problem(self.svm_type):
for i, (sv, kernel) in \
enumerate(izip(self.support_vectors, self.kernels)):
kvalue = N.empty((len(self.support_vectors),))
kvalue[i] = svm_node_dot(x, sv, kernel)
kvalue -= self.rho
return kvalue
else:
sv = self.support_vectors[0]
kernel = self.kernels[0]
kvalue = svm_node_dot(x, sv, kernel) - self.rho
return kvalue
def _create_gramvec(self, x):
gramvec = N.zeros((len(self.dataset)+1,),
dtype=libsvm.svm_node_dtype)
for sv_id in self.sv_ids:
sv = self.dataset[sv_id]
gramvec[sv_id]['value'] = svm_node_dot(x, sv, self.kernel)
return gramvec
def _create_gramvec(self, x):
gramvec = N.zeros((len(self.dataset)+1,),
dtype=libsvm.svm_node_dtype)
for sv_id in self.sv_ids:
sv = self.dataset[sv_id]
gramvec[sv_id]['value'] = svm_node_dot(x, sv, self.kernel)
return gramvec
