def fit(self, X, Y):
M = X[0].get_shape()[1] # number of features
N = len(X) # number of bags
F = np.random.ranf((1, M)) # regression hyperplane
H = np.matrix(np.zeros((N, M))) # bag representations
self.P = [] # instance weights
self.X_w = [] # flatten instances
self.Y_w = [] # flatten instance weights
converged = False
prev_error = sys.maxsize
it = 0
#print "-"*100
#print "e1: %f" % self.e1
#print "e2: %f" % self.e2
#print "e3: %f" % self.e3
#print "M: %d" % M
#print "N: %d" % N
print
print "[+] Training..."
print "--/start"
while (not converged and it < self.iterations):
for i, Xi in enumerate(X):
if it == 0:
if self.X_w == []:
self.X_w = Xi
else:
self.X_w = vstack([self.X_w, Xi])
self.P.append(np.ones((1, X[i].get_shape()[0])))
self.Y_w.append([])
Xi = Xi.tocsr()
if self.f2:
HC = np.matrix(self.f2.predict(Xi)).T
else:
HC = Xi.dot(F.T).T
self.f1 = cRLS(alpha=self.e1)
self.P[i] = self.f1.fit(HC, Y[i], self.P[i])
self.Y_w[i] = self.f1.coef_
cur_p = csr_matrix(self.f1.coef_)
H[i] = cur_p.dot(Xi).todense()
self.f2 = linear_model.Ridge(alpha=self.e2)
self.f2.fit(H, Y)
cur_error = mean_absolute_error(self.f2.predict(H), Y)
print "iteration %d -> (MAE: %f) " % (it, cur_error)
if prev_error - cur_error < 0.000001:
converged = True
prev_error = cur_error
it += 1
self.f3 = linear_model.Ridge(alpha=self.e3)
self.f3.fit(self.X_w, np.hstack(self.Y_w))
print "--/end"
def fit(self, X, Y): M = X[0].get_shape()[1] # number of features N = len(X) # number of bags F = np.random.ranf((1,M)) # regression hyperplane H = np.matrix(np.zeros((N,M))) # bag representations self.P = [] # instance weights self.X_w = [] # flatten instances self.Y_w = [] # flatten instance weights converged = False prev_error = sys.maxsize it = 0 #print "-"*100 #print "e1: %f" % self.e1 #print "e2: %f" % self.e2 #print "e3: %f" % self.e3 #print "M: %d" % M #print "N: %d" % N print print "[+] Training..." print "--/start" while(not converged and it < self.iterations): for i, Xi in enumerate(X): if it == 0: if self.X_w == []: self.X_w = Xi else: self.X_w = vstack([self.X_w, Xi]) self.P.append(np.ones((1,X[i].get_shape()[0]))) self.Y_w.append([]) Xi = Xi.tocsr() if self.f2: HC = np.matrix(self.f2.predict(Xi)).T else: HC = Xi.dot(F.T).T self.f1 = cRLS(alpha=self.e1) self.P[i] = self.f1.fit(HC,Y[i],self.P[i]) self.Y_w[i] = self.f1.coef_ cur_p = csr_matrix(self.f1.coef_) H[i] = cur_p.dot(Xi).todense() self.f2 = linear_model.Ridge(alpha=self.e2) self.f2.fit(H,Y) cur_error = mean_absolute_error(self.f2.predict(H),Y) print "iteration %d -> (MAE: %f) " % (it, cur_error) if prev_error - cur_error < 0.000001: converged = True prev_error = cur_error it += 1 self.f3 = linear_model.Ridge(alpha=self.e3) self.f3.fit(self.X_w,np.hstack(self.Y_w)) print "--/end"
def fit(self, X, Y): M = X[0].get_shape()[1] # number of features N = len(X) # number of instances F = np.random.ranf((1,M)) # hyperplane to be learned H = matrix(np.zeros((N,M))) # bag representations P = [] Y_w = [] X_w = [] converged = False prev_error = 999999 it = 0 print "-"*100 print "L1: %f" % self.l1 print "L2: %f" % self.l2 print "L3: %f" % self.l3 print "M: %d" % M print "N: %d" % N print print "[+] Training..." while(not converged and it < self.iterations): for i, Xi in enumerate(X): if it == 0: if X_w == []: X_w = Xi else: X_w = sparse.vstack([X_w, Xi ]) P.append(np.ones((1,X[i].get_shape()[0]))) Y_w.append([]) Xi = Xi.tocsr() if self.f2: HC = matrix(self.f2.predict(Xi)).T else: HC = Xi.dot(F.T).T self.f1 = cRLS(alpha=self.l1) P[i] = self.f1.fit(HC,Y[i],P[i]) Y_w[i] = self.f1.coef_ cur_p = sparse.csr_matrix(self.f1.coef_) H[i] = cur_p.dot(Xi).todense() self.f2 = linear_model.Ridge(alpha=self.l2) self.f2.fit(H,Y) pred = self.f2.predict(H) cur_error = mean_absolute_error(pred,Y) print "iteration %d -> (MAE: %f) " % (it, cur_error) self.coef_ = self.f2.coef_ if prev_error - cur_error < 0.000001: converged = True self.coef_ = self.f2.coef_ prev_error = cur_error it += 1 Y_w = np.hstack(Y_w) print "Training f3..." self.f3 = linear_model.Ridge(alpha=self.l3) self.f3.fit(X_w,Y_w) self.P = P self.H = H print "--/end" return F
def fit(self, X, Y):
M = X[0].get_shape()[1] # number of features
N = len(X) # number of instances
F = np.random.ranf((1, M)) # hyperplane to be learned
H = matrix(np.zeros((N, M))) # bag representations
P = []
Y_w = []
X_w = []
converged = False
prev_error = 999999
it = 0
print "-" * 100
print "L1: %f" % self.l1
print "L2: %f" % self.l2
print "L3: %f" % self.l3
print "M: %d" % M
print "N: %d" % N
print
print "[+] Training..."
while (not converged and it < self.iterations):
for i, Xi in enumerate(X):
if it == 0:
if X_w == []:
X_w = Xi
else:
X_w = sparse.vstack([X_w, Xi])
P.append(np.ones((1, X[i].get_shape()[0])))
Y_w.append([])
Xi = Xi.tocsr()
if self.f2:
HC = matrix(self.f2.predict(Xi)).T
else:
HC = Xi.dot(F.T).T
self.f1 = cRLS(alpha=self.l1)
P[i] = self.f1.fit(HC, Y[i], P[i])
Y_w[i] = self.f1.coef_
cur_p = sparse.csr_matrix(self.f1.coef_)
H[i] = cur_p.dot(Xi).todense()
self.f2 = linear_model.Ridge(alpha=self.l2)
self.f2.fit(H, Y)
pred = self.f2.predict(H)
cur_error = mean_absolute_error(pred, Y)
print "iteration %d -> (MAE: %f) " % (it, cur_error)
self.coef_ = self.f2.coef_
if prev_error - cur_error < 0.000001:
converged = True
self.coef_ = self.f2.coef_
prev_error = cur_error
it += 1
Y_w = np.hstack(Y_w)
print "Training f3..."
self.f3 = linear_model.Ridge(alpha=self.l3)
self.f3.fit(X_w, Y_w)
self.P = P
self.H = H
print "--/end"
return F
