def cost_func(B, model, profile, profiler):
cost = ml.zeros([model.T, 1])
U = ml.zeros([model.uDim, model.T])
T = model.T
for t in range(T - 1):
U[:, t] = B[0:model.xDim, t + 1] - B[0:model.xDim, t]
B[:, t + 1] = belief.belief_dynamics(B[:, t], U[:, t], None, model)
if max(U[:, t]) > 1:
cost[t] = 1000
elif abs(B[0, t]) > model.xMax[0]:
cost[t] = 1000
elif abs(B[1, t]) > model.xMax[1]:
cost[t] = 1000
else:
null, s = belief.decompose_belief(B[:, t], model)
cost[t] = model.alpha_belief * ml.trace(
s * s) + model.alpha_control * ml.sum(U[:, t].T * U[:, t])
x, s = belief.decompose_belief(B[:, T - 1], model)
cost[T - 1] = model.alpha_final_belief * ml.trace(s * s)
return cost, B, U
def cost_func(B,model,profile,profiler):
cost = ml.zeros([model.T,1])
U = ml.zeros([model.uDim,model.T])
T = model.T
for t in range(T-1):
U[:,t] = B[0:model.xDim,t+1]-B[0:model.xDim,t];
B[:,t+1] = belief.belief_dynamics(B[:,t],U[:,t],None,model);
if max(U[:,t])> 1:
cost[t] = 1000
elif abs(B[0,t]) > model.xMax[0]:
cost[t] = 1000
elif abs(B[1,t]) > model.xMax[1]:
cost[t] = 1000
else:
null, s = belief.decompose_belief(B[:,t], model)
cost[t] = model.alpha_belief*ml.trace(s*s)+model.alpha_control*ml.sum(U[:,t].T*U[:,t])
x, s = belief.decompose_belief(B[:,T-1], model)
cost[T-1] = model.alpha_final_belief*ml.trace(s*s)
return cost, B, U
def calculateQ(self, k):
baseQ = M(self.Q(k))
K = M(self.kf.K[k-1])
D = M(self.D[k])
Qest = K * D * K.T
#print D, K
f = mt.trace(Qest) / mt.trace(baseQ)
f = np.power(f, self.exponent)
f = min(10000000., f)
Q = baseQ * f
self.alpha[k] = f
return Q
def compute_merit(B, U, model, penalty_coeff):
xDim = model.xDim
T = model.T
merit = 0
for t in xrange(0,T-1):
x, s = belief.decompose_belief(B[:,t], model)
merit += model.alpha_belief*ml.trace(s*s)
merit += model.alpha_control*ml.sum(U[:,t].T*U[:,t])
merit += penalty_coeff*ml.sum(np.abs(B[:,t+1]-belief.belief_dynamics(B[:,t],U[:,t],None,model)))
x, s = belief.decompose_belief(B[:,T-1], model)
merit += model.alpha_final_belief*ml.trace(s*s)
return merit
def calculateQ(self, k):
Q = M(self.Q(k))
R = M(self.kf.R(k))
H = M(self.kf.H(k))
D = M(self.D[k-1])
alpha = np.trace(D - R) / np.trace(H * M(self.kf.Pminus[k-1]) * H.T)
alpha = np.asscalar(alpha)
if np.isfinite(alpha) and alpha>0:
alpha = np.power(alpha, self.exponent)
alpha = max(0.0001, min(alpha, 1000.*mt.trace(R) / mt.trace(Q)))
else:
alpha = 0.0001
Q = Q * alpha
self.alpha[k] = alpha
return Q
def compute_forward_simulated_cost(b, U, model):
T = model.T
cost = 0
b_t = b
for t in xrange(0,T-1):
x_t, s_t = decompose_belief(b_t, model)
cost += model.alpha_belief*ml.trace(s_t*s_t)
cost += model.alpha_control*ml.sum(U[:,t].T*U[:,t])
b_t = belief_dynamics(b_t, U[:,t], None, model)
x_T, s_T = decompose_belief(b_t, model)
cost += model.alpha_final_belief*ml.trace(s_T*s_T)
return cost
def compute_forward_simulated_cost(b, U, model):
T = model.T
cost = 0
b_t = b
for t in xrange(0, T - 1):
x_t, s_t = decompose_belief(b_t, model)
cost += model.alpha_belief * ml.trace(s_t * s_t)
cost += model.alpha_control * ml.sum(U[:, t].T * U[:, t])
b_t = belief_dynamics(b_t, U[:, t], None, model)
x_T, s_T = decompose_belief(b_t, model)
cost += model.alpha_final_belief * ml.trace(s_T * s_T)
return cost
def SPIRIT(A, lamb, energy, k0=1, holdOffTime=0, reorthog=False, evalMetrics="F"):
A = np.mat(A)
n = A.shape[1]
totalTime = A.shape[0]
Proj = npm.ones((totalTime, n)) * np.nan
recon = npm.zeros((totalTime, n))
# initialize w_i to unit vectors
W = npm.eye(n)
d = 0.01 * npm.ones((n, 1))
m = k0 # number of eigencomponents
relErrors = npm.zeros((totalTime, 1))
sumYSq = 0.0
E_t = []
sumXSq = 0.0
E_dash_t = []
res = {}
k_hist = []
W_hist = []
anomalies = []
# incremental update W
lastChangeAt = 0
for t in range(totalTime):
k_hist.append(m)
# update W for each y_t
x = A[t, :].T # new data as column vector
for j in range(m):
W[:, j], d[j], x = updateW(x, W[:, j], d[j], lamb)
Wj = W[:, j]
# Grams smit reorthog
if reorthog == True:
W[:, :m], R = npm.linalg.qr(W[:, :m])
# compute low-D projection, reconstruction and relative error
Y = W[:, :m].T * A[t, :].T # project to m-dimensional space
xActual = A[t, :].T # actual vector of the current time
xProj = W[:, :m] * Y # reconstruction of the current time
Proj[t, :m] = Y.T
recon[t, :] = xProj.T
xOrth = xActual - xProj
relErrors[t] = npm.sum(npm.power(xOrth, 2)) / npm.sum(npm.power(xActual, 2))
# update energy
sumYSq = lamb * sumYSq + npm.sum(npm.power(Y, 2))
E_dash_t.append(sumYSq)
sumXSq = lamb * sumXSq + npm.sum(npm.power(A[t, :], 2))
E_t.append(sumXSq)
# Record RSRE
if t == 0:
top = 0.0
bot = 0.0
top = top + npm.power(npm.linalg.norm(xActual - xProj), 2)
bot = bot + npm.power(npm.linalg.norm(xActual), 2)
new_RSRE = top / bot
if t == 0:
RSRE = new_RSRE
else:
RSRE = npm.vstack((RSRE, new_RSRE))
### Metric EVALUATION ###
# deviation from truth
if evalMetrics == "T":
Qt = W[:, :m]
if t == 0:
res["subspace_error"] = npm.zeros((totalTime, 1))
res["orthog_error"] = npm.zeros((totalTime, 1))
res["angle_error"] = npm.zeros((totalTime, 1))
Cov_mat = npm.zeros([n, n])
# Calculate Covarentce Matrix of data up to time t
Cov_mat = lamb * Cov_mat + npm.dot(xActual, xActual.T)
# Get eigenvalues and eigenvectors
WW, V = npm.linalg.eig(Cov_mat)
# Use this to sort eigenVectors in according to deccending eigenvalue
eig_idx = WW.argsort() # Get sort index
eig_idx = eig_idx[::-1] # Reverse order (default is accending)
# v_r = highest r eigen vectors (accoring to thier eigenvalue if sorted).
V_k = V[:, eig_idx[:m]]
# Calculate subspace error
C = npm.dot(V_k, V_k.T) - npm.dot(Qt, Qt.T)
res["subspace_error"][t, 0] = 10 * np.log10(npm.trace(npm.dot(C.T, C))) # frobenius norm in dB
# Calculate angle between projection matrixes
D = npm.dot(npm.dot(npm.dot(V_k.T, Qt), Qt.T), V_k)
eigVal, eigVec = npm.linalg.eig(D)
angle = npm.arccos(np.sqrt(max(eigVal)))
res["angle_error"][t, 0] = angle
# Calculate deviation from orthonormality
F = npm.dot(Qt.T, Qt) - npm.eye(m)
res["orthog_error"][t, 0] = 10 * np.log10(npm.trace(npm.dot(F.T, F))) # frobenius norm in dB
# Energy thresholding
######################
# check the lower bound of energy level
if sumYSq < energy[0] * sumXSq and lastChangeAt < t - holdOffTime and m < n:
lastChangeAt = t
m = m + 1
anomalies.append(t)
# print 'Increasing m to %d at time %d (ratio %6.2f)\n' % (m, t, 100 * sumYSq/sumXSq)
# check the upper bound of energy level
elif sumYSq > energy[1] * sumXSq and lastChangeAt < t - holdOffTime and m < n and m > 1:
lastChangeAt = t
m = m - 1
# print 'Decreasing m to %d at time %d (ratio %6.2f)\n' % (m, t, 100 * sumYSq/sumXSq)
W_hist.append(W[:, :m])
# set outputs
# Grams smit reorthog
if reorthog == True:
W[:, :m], R = npm.linalg.qr(W[:, :m])
# Data Stores
res2 = {
"hidden": Proj, # Array for hidden Variables
"E_t": np.array(E_t), # total energy of data
"E_dash_t": np.array(E_dash_t), # hidden var energy
"e_ratio": np.array(E_dash_t) / np.array(E_t), # Energy ratio
"rel_orth_err": relErrors, # orthoX error
"RSRE": RSRE, # Relative squared Reconstruction error
"recon": recon, # reconstructed data
"r_hist": k_hist, # history of r values
"W_hist": W_hist, # history of Weights
"anomalies": anomalies,
}
res.update(res2)
return res
def SPIRIT(streams, energyThresh, lamb, evalMetrics):
# Make
if type(streams) == np.ndarray:
streams_iter = iter(streams)
# Max No. Streams
if streams.ndim == 1:
streams = np.expand_dims(streams, axis=1)
num_streams = streams.shape[1]
else:
num_streams = streams.shape[1]
count_over = 0
count_under = 0
#===============================================================================
# Initalise k, w and d, lamb
#===============================================================================
k = 1 # Hidden Variables, initialise to one
# Weights
pc_weights = npm.zeros(num_streams)
pc_weights[0, 0] = 1
# initialise outputs
res = {}
all_weights = []
k_hist = []
anomalies = []
x_dash = npm.zeros((1,num_streams))
Eng = mat([0.00000001, 0.00000001])
E_xt = 0 # Energy of X at time t
E_rec_i = mat([0.000000000000001]) # Energy of reconstruction
Y = npm.zeros(num_streams)
timeSteps = streams.shape[0]
#===============================================================================
# Main Loop
#===============================================================================
for t in range(1, timeSteps + 1): # t = 1,...,200
k_hist.append(k)
x_t_plus_1 = mat(streams_iter.next()) # Read in next signals
d_i = E_rec_i * t
# Step 1 - Update Weights
pc_weights, y_t_i, error = track_W(x_t_plus_1,
k, pc_weights, d_i,
num_streams,
lamb)
# Record hidden variables
padding = num_streams - k
y_bar_t = npm.hstack((y_t_i, mat([nan] * padding)))
Y = npm.vstack((Y,y_bar_t))
# Record Weights
all_weights.append(pc_weights)
# Record reconstrunted z and RSRE
x_dash = npm.vstack((x_dash, y_t_i * pc_weights))
# Record RSRE
if t == 1:
top = 0.0
bot = 0.0
top = top + (norm(x_t_plus_1 - x_dash) ** 2 )
bot = bot + (norm(x_t_plus_1) ** 2)
new_RSRE = top / bot
if t == 1:
RSRE = new_RSRE
else:
RSRE = npm.vstack((RSRE, new_RSRE))
### FOR EVALUATION ###
#deviation from truth
if evalMetrics == 'T' :
Qt = pc_weights.T
if t == 1 :
res['subspace_error'] = npm.zeros((timeSteps,1))
res['orthog_error'] = npm.zeros((timeSteps,1))
res['angle_error'] = npm.zeros((timeSteps,1))
Cov_mat = npm.zeros([num_streams,num_streams])
# Calculate Covarentce Matrix of data up to time t
Cov_mat = lamb * Cov_mat + npm.dot(x_t_plus_1, x_t_plus_1.T)
# Get eigenvalues and eigenvectors
W , V = eig(Cov_mat)
# Use this to sort eigenVectors in according to deccending eigenvalue
eig_idx = W.argsort() # Get sort index
eig_idx = eig_idx[::-1] # Reverse order (default is accending)
# v_r = highest r eigen vectors (accoring to thier eigenvalue if sorted).
V_k = V[:, eig_idx[:k]]
# Calculate subspace error
C = npm.dot(V_k , V_k.T) - npm.dot(Qt , Qt.T)
res['subspace_error'][t-1,0] = 10 * np.log10(npm.trace(npm.dot(C.T , C))) #frobenius norm in dB
# Calculate angle between projection matrixes
D = npm.dot(npm.dot(npm.dot(V_k.T, Qt), Qt.T), V_k)
eigVal, eigVec = eig(D)
angle = npm.arccos(np.sqrt(max(eigVal)))
res['angle_error'][t-1,0] = angle
# Calculate deviation from orthonormality
F = npm.dot(Qt.T , Qt) - npm.eye(k)
res['orthog_error'][t-1,0] = 10 * np.log10(npm.trace(npm.dot(F.T , F))) #frobenius norm in dB
# Step 2 - Update Energy estimate
E_xt = ((lamb * (t-1) * E_xt) + norm(x_t_plus_1) ** 2) / t
for i in range(k):
E_rec_i[0, i] = ((lamb * (t-1) * E_rec_i[0, i]) + (y_t_i[0, i] ** 2)) / t
# Step 3 - Estimate the retained energy
E_retained = npm.sum(E_rec_i,1)
# Record Energy
Eng_new = npm.hstack((E_xt, E_retained[0,0]))
Eng = npm.vstack((Eng, Eng_new))
if E_retained < energyThresh[0] * E_xt:
if k != num_streams:
k = k + 1
# Initalise Ek+1 <-- 0
E_rec_i = npm.hstack((E_rec_i, mat([0])))
# Initialise W_i+1
new_weight_vec = npm.zeros(num_streams)
new_weight_vec[0, k-1] = 1
pc_weights = npm.vstack((pc_weights, new_weight_vec))
anomalies.append(t -1)
else:
count_over += 1
elif E_retained > energyThresh[1] * E_xt:
if k > 1 :
k = k - 1
# discard w_k and error
pc_weights = delete(pc_weights, -1, 0)
# Discard E_rec_i[k]
E_rec_i = delete(E_rec_i, -1)
else:
count_under += 1
# Data Stores
res2 = {'hidden' : Y, # Array for hidden Variables
'weights' : all_weights,
'E_t' : Eng[:,0], # total energy of data
'E_dash_t' : Eng[:,1], # hidden var energy
'e_ratio' : np.divide(Eng[:,1], Eng[:,0]), # Energy ratio
'RSRE' : RSRE, # Relative squared Reconstruction error
'recon' : x_dash, # reconstructed data
'r_hist' : k_hist, # history of r values
'anomalies' : anomalies}
res.update(res2)
return res, all_weights
def SPIRIT(A,
lamb,
energy,
k0=1,
holdOffTime=0,
reorthog=False,
evalMetrics='F'):
A = np.mat(A)
n = A.shape[1]
totalTime = A.shape[0]
Proj = npm.ones((totalTime, n)) * np.nan
recon = npm.zeros((totalTime, n))
# initialize w_i to unit vectors
W = npm.eye(n)
d = 0.01 * npm.ones((n, 1))
m = k0 # number of eigencomponents
relErrors = npm.zeros((totalTime, 1))
sumYSq = 0.
E_t = []
sumXSq = 0.
E_dash_t = []
res = {}
k_hist = []
W_hist = []
anomalies = []
# incremental update W
lastChangeAt = 0
for t in range(totalTime):
k_hist.append(m)
# update W for each y_t
x = A[t, :].T # new data as column vector
for j in range(m):
W[:, j], d[j], x = updateW(x, W[:, j], d[j], lamb)
Wj = W[:, j]
# Grams smit reorthog
if reorthog == True:
W[:, :m], R = npm.linalg.qr(W[:, :m])
# compute low-D projection, reconstruction and relative error
Y = W[:, :m].T * A[t, :].T # project to m-dimensional space
xActual = A[t, :].T # actual vector of the current time
xProj = W[:, :m] * Y # reconstruction of the current time
Proj[t, :m] = Y.T
recon[t, :] = xProj.T
xOrth = xActual - xProj
relErrors[t] = npm.sum(npm.power(xOrth, 2)) / npm.sum(
npm.power(xActual, 2))
# update energy
sumYSq = lamb * sumYSq + npm.sum(npm.power(Y, 2))
E_dash_t.append(sumYSq)
sumXSq = lamb * sumXSq + npm.sum(npm.power(A[t, :], 2))
E_t.append(sumXSq)
# Record RSRE
if t == 0:
top = 0.0
bot = 0.0
top = top + npm.power(npm.linalg.norm(xActual - xProj), 2)
bot = bot + npm.power(npm.linalg.norm(xActual), 2)
new_RSRE = top / bot
if t == 0:
RSRE = new_RSRE
else:
RSRE = npm.vstack((RSRE, new_RSRE))
### Metric EVALUATION ###
#deviation from truth
if evalMetrics == 'T':
Qt = W[:, :m]
if t == 0:
res['subspace_error'] = npm.zeros((totalTime, 1))
res['orthog_error'] = npm.zeros((totalTime, 1))
res['angle_error'] = npm.zeros((totalTime, 1))
Cov_mat = npm.zeros([n, n])
# Calculate Covarentce Matrix of data up to time t
Cov_mat = lamb * Cov_mat + npm.dot(xActual, xActual.T)
# Get eigenvalues and eigenvectors
WW, V = npm.linalg.eig(Cov_mat)
# Use this to sort eigenVectors in according to deccending eigenvalue
eig_idx = WW.argsort() # Get sort index
eig_idx = eig_idx[::-1] # Reverse order (default is accending)
# v_r = highest r eigen vectors (accoring to thier eigenvalue if sorted).
V_k = V[:, eig_idx[:m]]
# Calculate subspace error
C = npm.dot(V_k, V_k.T) - npm.dot(Qt, Qt.T)
res['subspace_error'][t, 0] = 10 * np.log10(
npm.trace(npm.dot(C.T, C))) #frobenius norm in dB
# Calculate angle between projection matrixes
D = npm.dot(npm.dot(npm.dot(V_k.T, Qt), Qt.T), V_k)
eigVal, eigVec = npm.linalg.eig(D)
angle = npm.arccos(np.sqrt(max(eigVal)))
res['angle_error'][t, 0] = angle
# Calculate deviation from orthonormality
F = npm.dot(Qt.T, Qt) - npm.eye(m)
res['orthog_error'][t, 0] = 10 * np.log10(
npm.trace(npm.dot(F.T, F))) #frobenius norm in dB
# Energy thresholding
######################
# check the lower bound of energy level
if sumYSq < energy[
0] * sumXSq and lastChangeAt < t - holdOffTime and m < n:
lastChangeAt = t
m = m + 1
anomalies.append(t)
# print 'Increasing m to %d at time %d (ratio %6.2f)\n' % (m, t, 100 * sumYSq/sumXSq)
# check the upper bound of energy level
elif sumYSq > energy[
1] * sumXSq and lastChangeAt < t - holdOffTime and m < n and m > 1:
lastChangeAt = t
m = m - 1
# print 'Decreasing m to %d at time %d (ratio %6.2f)\n' % (m, t, 100 * sumYSq/sumXSq)
W_hist.append(W[:, :m])
# set outputs
# Grams smit reorthog
if reorthog == True:
W[:, :m], R = npm.linalg.qr(W[:, :m])
# Data Stores
res2 = {
'hidden': Proj, # Array for hidden Variables
'E_t': np.array(E_t), # total energy of data
'E_dash_t': np.array(E_dash_t), # hidden var energy
'e_ratio': np.array(E_dash_t) / np.array(E_t), # Energy ratio
'rel_orth_err': relErrors, # orthoX error
'RSRE': RSRE, # Relative squared Reconstruction error
'recon': recon, # reconstructed data
'r_hist': k_hist, # history of r values
'W_hist': W_hist, # history of Weights
'anomalies': anomalies
}
res.update(res2)
return res
def SPIRIT(streams, energyThresh, lamb, evalMetrics):
# Make
if type(streams) == np.ndarray:
streams_iter = iter(streams)
# Max No. Streams
if streams.ndim == 1:
streams = np.expand_dims(streams, axis=1)
num_streams = streams.shape[1]
else:
num_streams = streams.shape[1]
count_over = 0
count_under = 0
#===============================================================================
# Initalise k, w and d, lamb
#===============================================================================
k = 1 # Hidden Variables, initialise to one
# Weights
pc_weights = npm.zeros(num_streams)
pc_weights[0, 0] = 1
# initialise outputs
res = {}
all_weights = []
k_hist = []
anomalies = []
x_dash = npm.zeros((1, num_streams))
Eng = mat([0.00000001, 0.00000001])
E_xt = 0 # Energy of X at time t
E_rec_i = mat([0.000000000000001]) # Energy of reconstruction
Y = npm.zeros(num_streams)
timeSteps = streams.shape[0]
#===============================================================================
# Main Loop
#===============================================================================
for t in range(1, timeSteps + 1): # t = 1,...,200
k_hist.append(k)
x_t_plus_1 = mat(streams_iter.next()) # Read in next signals
d_i = E_rec_i * t
# Step 1 - Update Weights
pc_weights, y_t_i, error = track_W(x_t_plus_1, k, pc_weights, d_i,
num_streams, lamb)
# Record hidden variables
padding = num_streams - k
y_bar_t = npm.hstack((y_t_i, mat([nan] * padding)))
Y = npm.vstack((Y, y_bar_t))
# Record Weights
all_weights.append(pc_weights)
# Record reconstrunted z and RSRE
x_dash = npm.vstack((x_dash, y_t_i * pc_weights))
# Record RSRE
if t == 1:
top = 0.0
bot = 0.0
top = top + (norm(x_t_plus_1 - x_dash)**2)
bot = bot + (norm(x_t_plus_1)**2)
new_RSRE = top / bot
if t == 1:
RSRE = new_RSRE
else:
RSRE = npm.vstack((RSRE, new_RSRE))
### FOR EVALUATION ###
#deviation from truth
if evalMetrics == 'T':
Qt = pc_weights.T
if t == 1:
res['subspace_error'] = npm.zeros((timeSteps, 1))
res['orthog_error'] = npm.zeros((timeSteps, 1))
res['angle_error'] = npm.zeros((timeSteps, 1))
Cov_mat = npm.zeros([num_streams, num_streams])
# Calculate Covarentce Matrix of data up to time t
Cov_mat = lamb * Cov_mat + npm.dot(x_t_plus_1, x_t_plus_1.T)
# Get eigenvalues and eigenvectors
W, V = eig(Cov_mat)
# Use this to sort eigenVectors in according to deccending eigenvalue
eig_idx = W.argsort() # Get sort index
eig_idx = eig_idx[::-1] # Reverse order (default is accending)
# v_r = highest r eigen vectors (accoring to thier eigenvalue if sorted).
V_k = V[:, eig_idx[:k]]
# Calculate subspace error
C = npm.dot(V_k, V_k.T) - npm.dot(Qt, Qt.T)
res['subspace_error'][t - 1, 0] = 10 * np.log10(
npm.trace(npm.dot(C.T, C))) #frobenius norm in dB
# Calculate angle between projection matrixes
D = npm.dot(npm.dot(npm.dot(V_k.T, Qt), Qt.T), V_k)
eigVal, eigVec = eig(D)
angle = npm.arccos(np.sqrt(max(eigVal)))
res['angle_error'][t - 1, 0] = angle
# Calculate deviation from orthonormality
F = npm.dot(Qt.T, Qt) - npm.eye(k)
res['orthog_error'][t - 1, 0] = 10 * np.log10(
npm.trace(npm.dot(F.T, F))) #frobenius norm in dB
# Step 2 - Update Energy estimate
E_xt = ((lamb * (t - 1) * E_xt) + norm(x_t_plus_1)**2) / t
for i in range(k):
E_rec_i[0, i] = ((lamb * (t - 1) * E_rec_i[0, i]) +
(y_t_i[0, i]**2)) / t
# Step 3 - Estimate the retained energy
E_retained = npm.sum(E_rec_i, 1)
# Record Energy
Eng_new = npm.hstack((E_xt, E_retained[0, 0]))
Eng = npm.vstack((Eng, Eng_new))
if E_retained < energyThresh[0] * E_xt:
if k != num_streams:
k = k + 1
# Initalise Ek+1 <-- 0
E_rec_i = npm.hstack((E_rec_i, mat([0])))
# Initialise W_i+1
new_weight_vec = npm.zeros(num_streams)
new_weight_vec[0, k - 1] = 1
pc_weights = npm.vstack((pc_weights, new_weight_vec))
anomalies.append(t - 1)
else:
count_over += 1
elif E_retained > energyThresh[1] * E_xt:
if k > 1:
k = k - 1
# discard w_k and error
pc_weights = delete(pc_weights, -1, 0)
# Discard E_rec_i[k]
E_rec_i = delete(E_rec_i, -1)
else:
count_under += 1
# Data Stores
res2 = {
'hidden': Y, # Array for hidden Variables
'weights': all_weights,
'E_t': Eng[:, 0], # total energy of data
'E_dash_t': Eng[:, 1], # hidden var energy
'e_ratio': np.divide(Eng[:, 1], Eng[:, 0]), # Energy ratio
'RSRE': RSRE, # Relative squared Reconstruction error
'recon': x_dash, # reconstructed data
'r_hist': k_hist, # history of r values
'anomalies': anomalies
}
res.update(res2)
return res, all_weights