def pre_compute_E_Beta(x, y, sig, kernel="RBF"):
"""
Function that pre computes the kernel eigenvalues/eigenfunctions during the cross-validation
Input:
x,y: the sample matrix and the label
sig: the value of the kernel parameters
Output:
E_: a list of eigenvalues
Beta_: a list of corresponding eigenvectors
"""
C = int(y.max())
eps = sp.finfo(sp.float64).eps
E_ = []
Beta_ = []
for i in range(C):
t = sp.where(y == (i + 1))[0]
ni = t.size
Ki = KERNEL()
Ki.compute_kernel(x[t, :], kernel=kernel, sig=sig)
Ki.center_kernel()
Ki.scale_kernel(ni)
E, Beta = linalg.eigh(Ki.K)
idx = E.argsort()[::-1]
E = E[idx]
E[E < eps] = eps
Beta = Beta[:, idx]
E_.append(E)
Beta_.append(Beta)
del E, Beta, Ki
return E_, Beta_
def pre_compute_E_Beta(x, y, sig, kernel='RBF'):
'''
Function that pre computes the kernel eigenvalues/eigenfunctions during the cross-validation
Input:
x,y: the sample matrix and the label
sig: the value of the kernel parameters
Output:
E_: a list of eigenvalues
Beta_: a list of corresponding eigenvectors
'''
C = int(y.max())
eps = sp.finfo(sp.float64).eps
E_ = []
Beta_ = []
for i in range(C):
t = sp.where(y == (i + 1))[0]
ni = t.size
Ki = KERNEL()
Ki.compute_kernel(x[t, :], kernel=kernel, sig=sig)
Ki.center_kernel()
Ki.scale_kernel(ni)
E, Beta = linalg.eigh(Ki.K)
idx = E.argsort()[::-1]
E = E[idx]
E[E < eps] = eps
Beta = Beta[:, idx]
E_.append(E)
Beta_.append(Beta)
del E, Beta, Ki
return E_, Beta_
def predict(self, xt, x, y, out_decision=None, out_proba=None):
"""
The function predicts the label for each sample with the learned model
Input:
xt: the test samples
x: the samples matrix of size n x d
y: the vector with label of size n
Output
yp: the label
D: the discriminant function
P: the posterior probabilities
"""
# Initialization
if isinstance(xt, sp.ndarray):
nt = xt.shape[0]
else:
nt = xt.K.shape[0]
C = int(y.max())
eps = sp.finfo(sp.float64).eps
dm = max(self.di)
D = sp.empty((nt, C))
Ki = KERNEL()
Kt = KERNEL()
kd = KERNEL()
for i in range(C):
t = sp.where(y == (i + 1))[0]
cst = sp.sum(sp.log(self.a[i])) + (self.ri[i] - self.di[i]) * sp.log(self.b[i]) - 2 * sp.log(self.prop[i])
if self.precomputed is None:
Ki.compute_kernel(x[t, :], kernel=self.kernel, sig=self.sig)
Kt.compute_kernel(xt, z=x[t, :], kernel=self.kernel, sig=self.sig)
kd.compute_diag_kernel(xt, kernel=self.kernel, sig=self.sig)
else:
Ki.K = x.K[t, :][:, t].copy()
Kt.K = xt.K[:, t].copy()
kd.K = xt.kd.copy()
Kt.center_kernel(Ko=Ki, kd=kd)
Ki.K = None
# Compute the decision rule
temp = sp.dot(Kt.K, self.A[i])
D[:, i] = sp.sum(Kt.K * temp, axis=1)
D[:, i] += kd.K * self.ib[i] + cst
# Check if negative value
if D.min() < 0:
D -= D.min()
yp = D.argmin(1) + 1
yp.shape = (nt, 1)
# Format the output
if out_proba is None:
if out_decision is None:
return yp
else:
return yp, D
else:
# Compute posterior !! Should be changed to a safe version
P = sp.exp(-0.5 * D)
P /= sp.sum(P, axis=1).reshape(nt, 1)
P[P < eps] = 0
return yp, D, P
def train(self, x, y, sig=None, dc=None, threshold=None, fast=None, E_=None, Beta_=None):
"""
The function trains the pgpda model using the training samples
Inputs:
x: the samples matrix of size n x d, for the precomputed case (self.precomputed==1), x is a KERNEL object.
y: the vector with label of size n
sig: the parameter of the kernel function
dc: the number of dimension of the singanl subspace
threshold: the value of the cummulative variance that should be reached
fast = option used to perform a fast CV: only the parameter dc/threshold is learn
Outputs:
None - The model is included/updated in the object
"""
# Initialization
n = y.shape[0]
C = int(y.max())
eps = sp.finfo(sp.float64).eps
list_model_dc = "NM1 NM3 NM4"
if (sig is None) and (self.sig is None):
self.sig = 0.5
elif self.sig is None:
self.sig = sig
if (dc is None) and (self.dc is None):
self.dc = 2
elif self.dc is None:
self.dc = dc
if (threshold is None) and (self.threshold is None):
self.threshold = 0.95
elif self.threshold is None:
self.threshold = threshold
# Check of consistent dimension
if list_model_dc.find(self.model) > -1:
for i in range(C):
ni = sp.size(sp.where(y == (i + 1))[0])
if self.dc >= ni - 1:
self.dc = ni - 2
# Estimate the parameters of each class
for i in range(C):
t = sp.where(y == (i + 1))[0]
self.ni.append(sp.size(t))
self.prop.append(float(self.ni[i]) / n)
if fast is None:
# Compute Mi
Ki = KERNEL()
if self.precomputed is None:
Ki.compute_kernel(x[t, :], kernel=self.kernel, sig=self.sig)
else:
Ki.K = x.K[t, :][:, t].copy()
Ki.rank = Ki.K.shape[0]
self.ri.append(Ki.rank - 1)
Ki.center_kernel()
Ki.scale_kernel(self.ni[i])
TraceKi = sp.trace(Ki.K)
# Eigenvalue decomposition
E, Beta = linalg.eigh(Ki.K)
idx = E.argsort()[::-1]
E = E[idx]
E[E < eps] = eps
Beta = Beta[:, idx]
else:
E = E_[i]
Beta = Beta_[i]
self.ri.append(E.size - 1)
TraceKi = sp.sum(E)
# Parameter estimation
if list_model_dc.find(self.model) == -1:
di = estim_d(E[0 : self.ri[i] - 1], self.threshold)
else:
di = self.dc
self.di.append(di)
self.a.append(E[0:di])
self.b.append((TraceKi - sp.sum(self.a[i])) / (self.ri[i] - di))
if self.b[i] < eps: # Sanity check for numerical precision
self.b[i] = eps
self.ib.append(1.0 / eps)
else:
self.ib.append(1 / self.b[i])
self.Beta.append(Beta[:, 0:di])
del Beta, E
# Finish the estimation for the different models
if self.model == "NM0" or self.model == "NM1":
for i in range(C):
# Compute the value of matrix A
temp = self.Beta[i] * ((1 / self.a[i] - self.ib[i]) / self.a[i]).reshape(self.di[i])
self.A.append(sp.dot(temp, self.Beta[i].T) / self.ni[i])
elif self.model == "NM2" or self.model == "NM3":
for i in range(C):
# Update the value of a
self.a[i][:] = sp.mean(self.a[i])
# Compute the value of matrix A
temp = self.Beta[i] * ((1 / self.a[i] - self.ib[i]) / self.a[i]).reshape(self.di[i])
self.A.append(sp.dot(temp, self.Beta[i].T) / self.ni[i])
elif self.model == "NM4":
# Compute the value of a
al = sp.zeros((self.dc))
for i in range(self.dc):
for j in range(C):
al[i] += self.prop[j] * self.a[j][i]
for i in range(C):
self.a[i] = al.copy()
temp = self.Beta[i] * ((1 / self.a[i] - self.ib[i]) / self.a[i]).reshape(self.di[i])
self.A.append(sp.dot(temp, self.Beta[i].T) / self.ni[i])
self.A = sp.asarray(self.A)
def predict(self, xt, x, y, out_decision=None, out_proba=None):
'''
The function predicts the label for each sample with the learned model
Input:
xt: the test samples
x: the samples matrix of size n x d
y: the vector with label of size n
Output
yp: the label
D: the discriminant function
P: the posterior probabilities
'''
# Initialization
if isinstance(xt, sp.ndarray):
nt = xt.shape[0]
else:
nt = xt.K.shape[0]
C = int(y.max())
eps = sp.finfo(sp.float64).eps
dm = max(self.di)
D = sp.empty((nt, C))
Ki = KERNEL()
Kt = KERNEL()
kd = KERNEL()
for i in range(C):
t = sp.where(y == (i + 1))[0]
cst = sp.sum(sp.log(
self.a[i])) + (self.ri[i] - self.di[i]) * sp.log(
self.b[i]) - 2 * sp.log(self.prop[i])
if self.precomputed is None:
Ki.compute_kernel(x[t, :], kernel=self.kernel, sig=self.sig)
Kt.compute_kernel(xt,
z=x[t, :],
kernel=self.kernel,
sig=self.sig)
kd.compute_diag_kernel(xt, kernel=self.kernel, sig=self.sig)
else:
Ki.K = x.K[t, :][:, t].copy()
Kt.K = xt.K[:, t].copy()
kd.K = xt.kd.copy()
Kt.center_kernel(Ko=Ki, kd=kd)
Ki.K = None
#Compute the decision rule
temp = sp.dot(Kt.K, self.A[i])
D[:, i] = sp.sum(Kt.K * temp, axis=1)
D[:, i] += kd.K * self.ib[i] + cst
# Check if negative value
if D.min() < 0:
D -= D.min()
yp = D.argmin(1) + 1
yp.shape = (nt, 1)
# Format the output
if out_proba is None:
if out_decision is None:
return yp
else:
return yp, D
else:
# Compute posterior !! Should be changed to a safe version
P = sp.exp(-0.5 * D)
P /= sp.sum(P, axis=1).reshape(nt, 1)
P[P < eps] = 0
return yp, D, P
def train(self,
x,
y,
sig=None,
dc=None,
threshold=None,
fast=None,
E_=None,
Beta_=None):
'''
The function trains the pgpda model using the training samples
Inputs:
x: the samples matrix of size n x d, for the precomputed case (self.precomputed==1), x is a KERNEL object.
y: the vector with label of size n
sig: the parameter of the kernel function
dc: the number of dimension of the singanl subspace
threshold: the value of the cummulative variance that should be reached
fast = option used to perform a fast CV: only the parameter dc/threshold is learn
Outputs:
None - The model is included/updated in the object
'''
# Initialization
n = y.shape[0]
C = int(y.max())
eps = sp.finfo(sp.float64).eps
list_model_dc = 'NM1 NM3 NM4'
if (sig is None) and (self.sig is None):
self.sig = 0.5
elif self.sig is None:
self.sig = sig
if (dc is None) and (self.dc is None):
self.dc = 2
elif self.dc is None:
self.dc = dc
if (threshold is None) and (self.threshold is None):
self.threshold = 0.95
elif self.threshold is None:
self.threshold = threshold
# Check of consistent dimension
if (list_model_dc.find(self.model) > -1):
for i in range(C):
ni = sp.size(sp.where(y == (i + 1))[0])
if self.dc >= ni - 1:
self.dc = ni - 2
# Estimate the parameters of each class
for i in range(C):
t = sp.where(y == (i + 1))[0]
self.ni.append(sp.size(t))
self.prop.append(float(self.ni[i]) / n)
if fast is None:
# Compute Mi
Ki = KERNEL()
if self.precomputed is None:
Ki.compute_kernel(x[t, :],
kernel=self.kernel,
sig=self.sig)
else:
Ki.K = x.K[t, :][:, t].copy()
Ki.rank = Ki.K.shape[0]
self.ri.append(Ki.rank - 1)
Ki.center_kernel()
Ki.scale_kernel(self.ni[i])
TraceKi = sp.trace(Ki.K)
# Eigenvalue decomposition
E, Beta = linalg.eigh(Ki.K)
idx = E.argsort()[::-1]
E = E[idx]
E[E < eps] = eps
Beta = Beta[:, idx]
else:
E = E_[i]
Beta = Beta_[i]
self.ri.append(E.size - 1)
TraceKi = sp.sum(E)
# Parameter estimation
if list_model_dc.find(self.model) == -1:
di = estim_d(E[0:self.ri[i] - 1], self.threshold)
else:
di = self.dc
self.di.append(di)
self.a.append(E[0:di])
self.b.append((TraceKi - sp.sum(self.a[i])) / (self.ri[i] - di))
if self.b[i] < eps: # Sanity check for numerical precision
self.b[i] = eps
self.ib.append(1.0 / eps)
else:
self.ib.append(1 / self.b[i])
self.Beta.append(Beta[:, 0:di])
del Beta, E
# Finish the estimation for the different models
if self.model == 'NM0' or self.model == 'NM1':
for i in range(C):
# Compute the value of matrix A
temp = self.Beta[i] * ((1 / self.a[i] - self.ib[i]) /
self.a[i]).reshape(self.di[i])
self.A.append(sp.dot(temp, self.Beta[i].T) / self.ni[i])
elif self.model == 'NM2' or self.model == 'NM3':
for i in range(C):
# Update the value of a
self.a[i][:] = sp.mean(self.a[i])
# Compute the value of matrix A
temp = self.Beta[i] * ((1 / self.a[i] - self.ib[i]) /
self.a[i]).reshape(self.di[i])
self.A.append(sp.dot(temp, self.Beta[i].T) / self.ni[i])
elif self.model == 'NM4':
# Compute the value of a
al = sp.zeros((self.dc))
for i in range(self.dc):
for j in range(C):
al[i] += self.prop[j] * self.a[j][i]
for i in range(C):
self.a[i] = al.copy()
temp = self.Beta[i] * ((1 / self.a[i] - self.ib[i]) /
self.a[i]).reshape(self.di[i])
self.A.append(sp.dot(temp, self.Beta[i].T) / self.ni[i])
self.A = sp.asarray(self.A)