def singleShiftRerun(parameter_string):
params = parameter_string.split(', ')
# Data Sets
baseLine = 0.0
num_streams = [int(params[0].split()[-1])] # n
SNRs = [int(params[1].split()[-1])] # snr
anomaly_lengths = [int(params[2].split()[-1])] # l
anomaly_magnitudes = [int(params[3].split()[-1])] # m
initial_conditions = 20 # i
# Algorithm Parameters
thresholds = params[4].split()[-1].split(',')
e_highs = [float(thresholds[1][0:-1])] # eh
e_lows = [float(thresholds[0][1:])] # el
alphas = [float(params[5].split()[-1])] # a
holdOffs = [int(params[6].split()[-1])] # h
# Algorithm flags
run_spirit = 0
run_frahst = 1
run_frahst_pedro = 0
#===============================================================================
# Initialise Data sets
#========-=======================================================================
# For Profiling
start = time.time()
for n in num_streams:
for snr in SNRs:
for l in anomaly_lengths:
for m in anomaly_magnitudes:
A = 0
for i in range(initial_conditions):
# Seed random number generator
np.random.seed(i)
# Two ts that have anomalous shift
s0 = Tseries(0)
s1 = Tseries(0)
s0.makeSeries([1,3,1],[100, l, 200 - l],[baseLine, baseLine, baseLine + m],
gradient = float(m)/float(l), noise_type ='none')
s1.makeSeries([1,4,1],[200, l, 100 - l],[baseLine, baseLine, baseLine - m],
gradient = float(m)/float(l), noise_type ='none')
# The rest of the ts
for k in range(2, n) :
name = 's'+ str(k)
vars()[name] = Tseries(0)
vars()[name].makeSeries([1], [300], [baseLine], noise_type ='none')
# Concat into one matrix
S = scipy.c_[s0]
for k in range(1, n) :
S = scipy.c_[ S, vars()['s'+ str(k)] ]
# Concatonate to 3d array, timesteps x streams x initial condition
if type(A) == int:
A = S
else:
A = np.dstack((A, S))
# Calculate the noise
Ps = np.sum(A[:,:,0] ** 2)
Pn = Ps / (10. ** (snr/10.))
scale = Pn / (n * 300.)
noise = np.random.randn(A.shape[0], A.shape[1], A.shape[2]) * np.sqrt(scale)
A = A + noise
#===============================================================================
# Ground Truths
#===============================================================================
# # time step | length
ground_truths = np.array([[100, l],
[200, l]])
#==============================================================================
# Run Algorithm
#==============================================================================
alg_count = 1
for eh in e_highs :
for el in e_lows :
for a in alphas :
for h in holdOffs :
print 'Running Algorithm(s) with:\nE_Thresh = (' + str(el) + ',' + str(eh) + ')\n' + \
'alpha = ' + str(a) + '\nHoldOff = ' + str(h)
SPIRIT_metricList = []
FRAHST_metricList = []
PEDRO_FRAHST_metricList = []
for i in range(initial_conditions):
# Load Data
streams = A[:,:,i]
if run_spirit == 1:
# SPIRIT
res_sp = SPIRIT(streams, a, [el, eh], evalMetrics = 'F',
reorthog = False, holdOffTime = h)
res_sp['Alg'] = 'SPIRIT: alpha = ' + str(a) + \
' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
pltSummary2(res_sp, streams, (el, eh))
SPIRIT_metricList.append(analysis(res_sp, ground_truths, 300 ))
# data = res_sp
# Title = 'SPIRIT alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
#
# plot_4x1(streams, data['hidden'], data['e_ratio'], data['orthog_error'],
# ['Input Data','Hidden\nVariables',
# 'Energy Ratio', 'Orthogonality\nError (dB)'] , 'Time Steps', Title)
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
#
if run_frahst == 1:
# My version of Frahst
res_fr = FRAHST_V3_1(streams, alpha=a, e_low=el, e_high=eh,
holdOffTime=h, fix_init_Q = 1, r = 1, evalMetrics = 'F')
res_fr['Alg'] = 'MyFrahst: alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
FRAHST_metricList.append(analysis(res_fr, ground_truths, 300 ))
pltSummary2(res_fr, streams, (el, eh))
#
# data = res_fr
# Title = 'My Frahst with alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
if run_frahst_pedro == 1:
# Pedros version of Frahst
res_frped = frahst_pedro(streams, alpha=a, e_low=el, e_high=eh,
holdOffTime=h, r = 1, evalMetrics = 'F')
res_frped['Alg'] = 'Pedros Frahst: alpha = ' + str(a) + \
' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
PEDRO_FRAHST_metricList.append(analysis(res_frped, ground_truths, 300 ))
pltSummary2(res_frped, streams, (el, eh))
#
# data = res_frped
# Title = 'Pedros Frahst: alpha = ' + str(a) + \
# ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
#
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
finish = time.time() - start
print 'Runtime = ' + str(finish) + 'seconds\n'
print 'In H:M:S = ' + GetInHMS(finish)
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 05 23:19:11 2011
THIS IS THE VERSION TO RUN ON MAC, NOT WINDOWS
@author: musselle
"""
from numpy import eye, zeros, dot, sqrt, log10, trace, arccos, nan, arange, ones
import numpy as np
import scipy as sp
import numpy.random as npr
from numpy.linalg import qr, eig, norm, solve
from matplotlib.pyplot import plot, figure, title, step, ylim
from artSigs import genCosSignals_no_rand , genCosSignals
import scipy.io as sio
from utils import analysis, QRsolveA, pltSummary2
from PedrosFrahst import frahst_pedro_original
from Frahst_v3_1 import FRAHST_V3_1
from Frahst_v3_3 import FRAHST_V3_3
from Frahst_v3_4 import FRAHST_V3_4
from Frahst_v4_0 import FRAHST_V4_0
from load_syn_ping_data import load_n_store
from QR_eig_solve import QRsolve_eigV
from create_Abilene_links_data import create_abilene_links_data
from MAfunctions import MA_over_window
from ControlCharts import Tseries
def FRAHST_V6_3(data, r=1, alpha=0.96, L = 1, holdOffTime=0, evalMetrics = 'F',
EW_mean_alpha = 0.1, EWMA_filter_alpha = 0.3, residual_thresh = 0.1,
F_min = 0.9, epsilon = 0.05,
static_r = 0, r_upper_bound = None,
v3_4 = 0
v3_3 = 0
v3_1 = 0
pedro = 0
if v6_4:
'''EWMA detection with F_min-epsilon thresholding'''
res_v6_4 = FRAHST_V6_4(data, r=1, alpha=alpha, L = L, holdOffTime=holdOFF, evalMetrics = 'F',
EW_mean_alpha = EW_mean_alpha, EWMA_filter_alpha = EWMA_filter_alpha,
residual_thresh = residual_thresh,
F_min = F_min, epsilon = epsilon,
static_r = 0, r_upper_bound = None,
fix_init_Q = 1, ignoreUp2 = ignoreUp2)
res_v6_4['Alg'] = 'My FRAUST V6.4 Eigen-Adaptive'
pltSummary2(res_v6_4, data, (F_min + epsilon, F_min))
ylim(F_min - 0.05 , 1.02)
if v6_4_fix:
'''EWMA detection with F_min-epsilon thresholding'''
res_v6_4f = FRAHST_V6_4(data, r=R, alpha=alpha, L = L, holdOffTime=holdOFF, evalMetrics = 'F',
EW_mean_alpha = EW_mean_alpha, EWMA_filter_alpha = EWMA_filter_alpha,
residual_thresh = residual_thresh,
F_min = F_min, epsilon = epsilon,
static_r = 1, r_upper_bound = None,
fix_init_Q = 1, ignoreUp2 = ignoreUp2)
res_v6_4f['Alg'] = 'My FRAUST V6.4 Fixed'
pltSummary2(res_v6_4f, data, (F_min + epsilon, F_min))
ylim(F_min - 0.05 , 1.02)
if run_spirit == 1:
# SPIRIT
res_sp = SPIRIT(
streams, a, [el, eh], evalMetrics="F", reorthog=False, holdOffTime=h
)
res_sp["Alg"] = (
"SPIRIT: alpha = "
+ str(a)
+ " ,E_Thresh = ("
+ str(el)
+ ","
+ str(eh)
+ ")"
)
pltSummary2(res_sp, streams, (el, eh))
SPIRIT_RawResults.append(res_sp)
SPIRIT_metricList.append(analysis(res_sp, ground_truths, 3 * interval_length))
# data = res_sp
# Title = 'SPIRIT alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
#
# plot_4x1(streams, data['hidden'], data['e_ratio'], data['orthog_error'],
# ['Input Data','Hidden\nVariables',
# 'Energy Ratio', 'Orthogonality\nError (dB)'] , 'Time Steps', Title)
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
alpha=alpha,
L=L,
holdOffTime=holdOFF,
evalMetrics='F',
EW_mean_alpha=EW_mean_alpha,
EWMA_filter_alpha=EWMA_filter_alpha,
residual_thresh=residual_thresh,
F_min=F_min,
epsilon=epsilon,
static_r=0,
r_upper_bound=None,
fix_init_Q=1,
ignoreUp2=ignoreUp2)
res_v6_4['Alg'] = 'My FRAUST V6.4 Eigen-Adaptive'
pltSummary2(res_v6_4, data, (F_min + epsilon, F_min))
ylim(F_min - 0.05, 1.02)
if v6_4_fix:
'''EWMA detection with F_min-epsilon thresholding'''
res_v6_4f = FRAHST_V6_4(data,
r=R,
alpha=alpha,
L=L,
holdOffTime=holdOFF,
evalMetrics='F',
EW_mean_alpha=EW_mean_alpha,
EWMA_filter_alpha=EWMA_filter_alpha,
residual_thresh=residual_thresh,
F_min=F_min,
epsilon=epsilon,
# Use Passive Normal
#data, ant, time = prime_data('SYN', 'PN', useCol = range(2,8), use2time = 7000)
"""PARAMETERS"""
e_high = 0.98
e_low = 0.85
alpha = 0.9
holdOFF = 0
'''My latest version'''
res_new = FRAHST_V3_3(data, alpha=alpha, e_low=e_low, e_high=e_high,
holdOffTime=holdOFF, fix_init_Q = 1, r = 1, evalMetrics = 'F')
res_new['Alg'] = 'My Latest Implimentation of FRAUST '
pltSummary2(res_new, data, (e_high, e_low))
'''My older version'''
res_old = FRAHST_V3_1(data, alpha=alpha, e_low=e_low, e_high=e_high,
holdOffTime=holdOFF, fix_init_Q = 1, r = 1, evalMetrics = 'F')
res_old['Alg'] = 'My Older Implimentation of FRAUST '
pltSummary2(res_old, data, (e_high, e_low))
'''Pedros Version'''
res_ped = frahst_pedro_original(data, r=1, alpha=alpha, e_low=e_low, e_high=e_high,
holdOffTime=holdOFF, evalMetrics='F')
res_ped['Alg'] = 'Pedros Original Implimentation of FRAUST'
pltSummary2(res_ped, data, (e_high, e_low))
if run_spirit == 1:
# SPIRIT
res_sp = SPIRIT(streams,
alpha, [e_low, e_high],
evalMetrics='T',
reorthog=False,
holdOffTime=holdOff)
res_sp['Alg'] = 'SPIRIT with alpha = ' + str(
valA) + ' and e_low = ' + str(valB)
#plot_31(res_sp['RSRE'], res_sp['e_ratio'], res_sp['orthog_error'],
# ['RSRE','Energy Ratio','Orthogonality\nError (dB)'], 'Time Steps',
# 'Error Analysis of SPIRIT' )
pltSummary2(res_sp, streams, (e_low, e_high))
data = res_sp
Title = 'SPIRIT with alpha = ' + str(valA) + ' and e_low = ' + str(
valB)
plot_4x1(streams, data['hidden'], data['e_ratio'],
data['orthog_error'], [
'Input Data', 'Hidden\nVariables', 'Energy Ratio',
'Orthogonality\nError (dB)'
], 'Time Steps', Title)
#TODO
# Why does changing the above for this
plot_4x1(streams, data['hidden'], data['orthog_error'],
streams = streams - Maved_data_MAwindow
if run_spirit == 1:
# SPIRIT
res_sp = SPIRIT(streams, a, [el, eh], evalMetrics = 'F',
reorthog = False, holdOffTime = h)
res_sp['Alg'] = 'SPIRIT: alpha = ' + str(a) + \
' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
# Store
algInfo = 'SPIRIT: E_Thresh = (' + str(el) + ',' + str(eh) + ')\n' + \
'alpha = ' + str(a) + '\nHoldOff = ' + str(h)
results.append({'info': {'data': dataInfo, 'alg' : algInfo},
'data' : res_sp})
# Plot
pltSummary2(res_sp, streams, (el, eh))
if run_frahst_v5_0 == 1:
# Multiple Auto Correlation Frahsts
for n in range(streams.shape[1]):
res_name = 'res_ACF' + str(n)
vars()[res_name] = FRAHST_V5_0(streams, data_column = n, L = win_L, alpha=a, e_low=el, e_high=eh,
holdOffTime=h, fix_init_Q = 1, r = 1, evalMetrics = 'T',
ignoreUp2 = ignoreUp2, static_r = 0, r_upper_bound = upper_bound)
vars()[res_name]['Alg'] = res_name + 'alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
pltSummary2(vars()[res_name], streams[:,n] , (eh, el))
# Store
algInfo = res_name + ': E_Thresh = (' + str(el) + ',' + str(eh) + ')\n' + \
'alpha = ' + str(a) + '\nHoldOff = ' + str(h)
results.append({'info': {'data': dataInfo, 'alg' : algInfo},
streams, 0.9)
original = streams
streams = streams - Maved_data_MAwindow
if run_spirit == 1:
# SPIRIT
res_sp = SPIRIT(streams,
a, [el, eh],
evalMetrics='F',
reorthog=False,
holdOffTime=h)
res_sp['Alg'] = 'SPIRIT: alpha = ' + str(a) + \
' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
pltSummary2(res_sp, streams, (el, eh))
SPIRIT_RawResults.append(res_sp)
SPIRIT_metricList.append(
analysis(res_sp, ground_truths,
3 * interval_length))
# data = res_sp
# Title = 'SPIRIT alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
#
# plot_4x1(streams, data['hidden'], data['e_ratio'], data['orthog_error'],
# ['Input Data','Hidden\nVariables',
# 'Energy Ratio', 'Orthogonality\nError (dB)'] , 'Time Steps', Title)
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
res['anomalies'] = []
else:
# stack values for all keys
for k in tracked_values:
res[k] = np.vstack((res[k], st[k]))
if st['anomaly'] == True:
print 'Found Anomaly at t = {0}'.format(st['t'])
res['anomalies'].append(st['t'])
# increment time
st['t'] += 1
res['Alg'] = 'My FRAHST'
res['hidden'] = res['ht']
res['r_hist'] = res['r']
pltSummary2(res, data, (p['F_min'] + p['epsilon'], p['F_min']))
#res['rec_dsn'] = res['pred_dsn'] # only needed to test prediction error t stat method
plot_4x1(data, res['ht'], res['rec_dsn'], res['t_stat'], ['']*4, ['']*4)
plt.hlines(p['x_thresh'], 0, 1000)
plt.hlines(-p['x_thresh'], 0, 1000)
plt.ylim(-p['x_thresh']-5, p['x_thresh']+5)
#N = 20
#a = res['recon_err_norm']**2
#b = np.zeros_like(a)
#for i in range(1,len(a)):
#b[i] = a[i] - a[i-1]
def singleShiftRerun(parameter_string):
params = parameter_string.split(', ')
# Data Sets
baseLine = 0.0
num_streams = [int(params[0].split()[-1])] # n
SNRs = [int(params[1].split()[-1])] # snr
anomaly_lengths = [int(params[2].split()[-1])] # l
anomaly_magnitudes = [int(params[3].split()[-1])] # m
initial_conditions = 20 # i
# Algorithm Parameters
thresholds = params[4].split()[-1].split(',')
e_highs = [float(thresholds[1][0:-1])] # eh
e_lows = [float(thresholds[0][1:])] # el
alphas = [float(params[5].split()[-1])] # a
holdOffs = [int(params[6].split()[-1])] # h
# Algorithm flags
run_spirit = 0
run_frahst = 1
run_frahst_pedro = 0
#===============================================================================
# Initialise Data sets
#========-=======================================================================
# For Profiling
start = time.time()
for n in num_streams:
for snr in SNRs:
for l in anomaly_lengths:
for m in anomaly_magnitudes:
A = 0
for i in range(initial_conditions):
# Seed random number generator
np.random.seed(i)
# Two ts that have anomalous shift
s0 = Tseries(0)
s1 = Tseries(0)
s0.makeSeries([1, 3, 1], [100, l, 200 - l],
[baseLine, baseLine, baseLine + m],
gradient=float(m) / float(l),
noise_type='none')
s1.makeSeries([1, 4, 1], [200, l, 100 - l],
[baseLine, baseLine, baseLine - m],
gradient=float(m) / float(l),
noise_type='none')
# The rest of the ts
for k in range(2, n):
name = 's' + str(k)
vars()[name] = Tseries(0)
vars()[name].makeSeries([1], [300], [baseLine],
noise_type='none')
# Concat into one matrix
S = scipy.c_[s0]
for k in range(1, n):
S = scipy.c_[S, vars()['s' + str(k)]]
# Concatonate to 3d array, timesteps x streams x initial condition
if type(A) == int:
A = S
else:
A = np.dstack((A, S))
# Calculate the noise
Ps = np.sum(A[:, :, 0]**2)
Pn = Ps / (10.**(snr / 10.))
scale = Pn / (n * 300.)
noise = np.random.randn(A.shape[0], A.shape[1],
A.shape[2]) * np.sqrt(scale)
A = A + noise
#===============================================================================
# Ground Truths
#===============================================================================
# # time step | length
ground_truths = np.array([[100, l], [200, l]])
#==============================================================================
# Run Algorithm
#==============================================================================
alg_count = 1
for eh in e_highs:
for el in e_lows:
for a in alphas:
for h in holdOffs:
print 'Running Algorithm(s) with:\nE_Thresh = (' + str(el) + ',' + str(eh) + ')\n' + \
'alpha = ' + str(a) + '\nHoldOff = ' + str(h)
SPIRIT_metricList = []
FRAHST_metricList = []
PEDRO_FRAHST_metricList = []
for i in range(initial_conditions):
# Load Data
streams = A[:, :, i]
if run_spirit == 1:
# SPIRIT
res_sp = SPIRIT(streams,
a, [el, eh],
evalMetrics='F',
reorthog=False,
holdOffTime=h)
res_sp['Alg'] = 'SPIRIT: alpha = ' + str(a) + \
' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
pltSummary2(
res_sp, streams, (el, eh))
SPIRIT_metricList.append(
analysis(
res_sp, ground_truths,
300))
# data = res_sp
# Title = 'SPIRIT alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
#
# plot_4x1(streams, data['hidden'], data['e_ratio'], data['orthog_error'],
# ['Input Data','Hidden\nVariables',
# 'Energy Ratio', 'Orthogonality\nError (dB)'] , 'Time Steps', Title)
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
#
if run_frahst == 1:
# My version of Frahst
res_fr = FRAHST_V3_1(
streams,
alpha=a,
e_low=el,
e_high=eh,
holdOffTime=h,
fix_init_Q=1,
r=1,
evalMetrics='F')
res_fr[
'Alg'] = 'MyFrahst: alpha = ' + str(
a
) + ' ,E_Thresh = (' + str(
el) + ',' + str(eh) + ')'
FRAHST_metricList.append(
analysis(
res_fr, ground_truths,
300))
pltSummary2(
res_fr, streams, (el, eh))
#
# data = res_fr
# Title = 'My Frahst with alpha = ' + str(a) + ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
if run_frahst_pedro == 1:
# Pedros version of Frahst
res_frped = frahst_pedro(
streams,
alpha=a,
e_low=el,
e_high=eh,
holdOffTime=h,
r=1,
evalMetrics='F')
res_frped['Alg'] = 'Pedros Frahst: alpha = ' + str(a) + \
' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
PEDRO_FRAHST_metricList.append(
analysis(
res_frped, ground_truths,
300))
pltSummary2(
res_frped, streams, (el, eh))
#
# data = res_frped
# Title = 'Pedros Frahst: alpha = ' + str(a) + \
# ' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
#
# plot_4x1(streams, data['hidden'], data['orthog_error'], data['subspace_error'],
# ['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
# 'Time Steps', Title)
finish = time.time() - start
print 'Runtime = ' + str(finish) + 'seconds\n'
print 'In H:M:S = ' + GetInHMS(finish)
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 05 23:19:11 2011
THIS IS THE VERSION TO RUN ON MAC, NOT WINDOWS
@author: musselle
"""
from numpy import eye, zeros, dot, sqrt, log10, trace, arccos, nan, arange, ones
import numpy as np
import scipy as sp
import numpy.random as npr
from numpy.linalg import qr, eig, norm, solve
from matplotlib.pyplot import plot, figure, title, step, ylim
from artSigs import genCosSignals_no_rand , genCosSignals
import scipy.io as sio
from utils import analysis, QRsolveA, pltSummary2
from PedrosFrahst import frahst_pedro_original
from Frahst_v3_1 import FRAHST_V3_1
from Frahst_v3_3 import FRAHST_V3_3
from Frahst_v3_4 import FRAHST_V3_4
from Frahst_v4_0 import FRAHST_V4_0
from load_syn_ping_data import load_n_store
from QR_eig_solve import QRsolve_eigV
from create_Abilene_links_data import create_abilene_links_data
from MAfunctions import MA_over_window
def FRAHST_V6_2(data, r=1, alpha=0.96, EW_mean_alpha = 0.1, EWMA_filter_alpha = 0.3,
residual_thresh = 0.1, epsilon = 0.05,
e_low = 0.96, e_high = 0.98, fix_init_Q = 0, holdOffTime=0,
evalMetrics = 'F', static_r = 0, r_upper_bound = None, L = 5, ignoreUp2 = 0,
data_norm_window = 50):
"""
res['anomalies'] = []
else:
# stack values for all keys
for k in tracked_values:
res[k] = np.vstack((res[k], st[k]))
if st['anomaly'] == True:
print 'Found Anomaly at t = {0}'.format(st['t'])
res['anomalies'].append(st['t'])
# increment time
st['t'] += 1
res['Alg'] = 'My FRAHST'
res['hidden'] = res['ht']
res['r_hist'] = res['r']
pltSummary2(res, data, (p['F_min'] + p['epsilon'], p['F_min']))
#res['rec_dsn'] = res['pred_dsn'] # only needed to test prediction error t stat method
plot_4x1(data, res['ht'], res['rec_dsn'], res['t_stat'], [''] * 4,
[''] * 4)
plt.hlines(p['x_thresh'], 0, 1000)
plt.hlines(-p['x_thresh'], 0, 1000)
plt.ylim(-p['x_thresh'] - 5, p['x_thresh'] + 5)
#N = 20
#a = res['recon_err_norm']**2
#b = np.zeros_like(a)
#for i in range(1,len(a)):
alpha = valA
e_low = valB
print 'Running Alpha =', valA, ' and e_low = ', valB
if run_spirit == 1:
# SPIRIT
res_sp = SPIRIT(streams, alpha, [e_low, e_high], evalMetrics = 'F',
reorthog = False, holdOffTime = holdOff)
res_sp['Alg'] = 'SPIRIT with alpha = ' + str(valA) + ' and e_low = ' + str(valB)
#plot_31(res_sp['RSRE'], res_sp['e_ratio'], res_sp['orthog_error'],
# ['RSRE','Energy Ratio','Orthogonality\nError (dB)'], 'Time Steps',
# 'Error Analysis of SPIRIT' )
pltSummary2(res_sp, streams, (e_low, e_high))
if run_frahst == 1:
# My version of Frahst
res_fr = FRAHST_V3_1(streams, alpha=alpha, e_low=e_low, e_high=e_high,
holdOffTime=holdOff, fix_init_Q = 1, r = 1, evalMetrics = 'F')
res_fr['Alg'] = 'Frahst with alpha = ' + str(valA) + ' and e_low = ' + str(valB)
#plot_31(res_fr['RSRE'], res_fr['e_ratio'], res_fr['orthog_error'],
# ['RSRE','Energy Ratio','Orthogonality\nError (dB)'], 'Time Steps',
# 'Error Analysis of FRAHST' )
# metric_me, sets_me, anom_det_tab_me = analysis(res_me, AbileneMat['P_g_truth_tab'], n)
pltSummary2(res_fr, streams, (e_low, e_high))
'r_hist': k_hist, # history of r values
'W_hist': W_hist, # history of Weights
'anomalies': anomalies
}
res.update(res2)
return res
if __name__ == '__main__':
#A1 = np.mat(np.sin(np.arange(1,1001)/20.0)).T
#A11 = A1 * npm.rand((1,10))
#plt.figure()
#plt.plot(A11)
#a1 = np.sin(npm.arange(1,1001)/10.0)
#a2 = np.sin(npm.arange(1,1001)/50.0)
#A2 = np.mat([a1,a2]).T * npm.rand((2,10))
#plt.figure()
#plt.plot(A2)
data = load_data('chlorine')
data[1500:2000, 0:4] = 0.7
data = data[:, :15]
res = SPIRIT(data, 1, [0.8, 0.99])
res['Alg'] = 'SPIRIT'
pltSummary2(res, data, (0.8, 0.99))
holdOffTime=h)
res_sp['Alg'] = 'SPIRIT: alpha = ' + str(a) + \
' ,E_Thresh = (' + str(el) + ',' + str(eh) + ')'
# Store
algInfo = 'SPIRIT: E_Thresh = (' + str(el) + ',' + str(eh) + ')\n' + \
'alpha = ' + str(a) + '\nHoldOff = ' + str(h)
results.append({
'info': {
'data': dataInfo,
'alg': algInfo
},
'data': res_sp
})
# Plot
pltSummary2(
res_sp, streams, (el, eh))
if run_frahst_v5_0 == 1:
# Multiple Auto Correlation Frahsts
for n in range(streams.shape[1]):
res_name = 'res_ACF' + str(n)
vars()[res_name] = FRAHST_V5_0(
streams,
data_column=n,
L=win_L,
alpha=a,
e_low=el,
e_high=eh,
holdOffTime=h,
fix_init_Q=1,
r=1,
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 05 23:19:11 2011
THIS IS THE VERSION TO RUN ON MAC, NOT WINDOWS
@author: musselle
"""
from numpy import eye, zeros, dot, sqrt, log10, trace, arccos, nan, arange, ones
import numpy as np
from numpy.random import rand
from numpy.linalg import qr, eig, norm, solve
from matplotlib.pyplot import plot, figure, title, step
from artSigs import genCosSignals_no_rand , genCosSignals
import scipy.io as sio
from utils import analysis, QRsolveA, pltSummary2
from PedrosFrahst import frahst_pedro_original
from Frahst_v3_1 import FRAHST_V3_1
from Frahst_v3_3 import FRAHST_V3_3
from Frahst_v3_4 import FRAHST_V3_4
from load_syn_ping_data import load_n_store
from QR_eig_solve import QRsolve_eigV
def FRAHST_V4_0(data, r=1, alpha=0.96, e_low=0.96, e_high=0.98, fix_init_Q = 0, holdOffTime=0,
evalMetrics = 'F', static_r = 0, r_upper_bound = None, L = 5, ignoreUp2 = 0):
"""
Fast Rank Adaptive Householder Subspace Tracking Algorithm (FRAHST)
Version 4.0 - Now also approximates eigenvalues for the approximated tracked basis for the eignevectors
- Approach uses an orthogonal iteration arround X.T
- Note, only a good approximation if alpha ~< 1. Used as its the fastest method
as X.T b --> b must be solved anyway.
- New entries in res
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 05 23:19:11 2011
THIS IS THE VERSION TO RUN ON MAC, NOT WINDOWS
@author: musselle
"""
from numpy import eye, zeros, dot, sqrt, log10, trace, arccos, nan, arange, ones
import numpy as np
import scipy as sp
from numpy.random import rand
from numpy.linalg import qr, eig, norm, solve
from matplotlib.pyplot import plot, figure, title, step
from artSigs import genCosSignals_no_rand , genCosSignals
import scipy.io as sio
from utils import analysis, QRsolveA, pltSummary2
from PedrosFrahst import frahst_pedro_original
from Frahst_v3_1 import FRAHST_V3_1
from Frahst_v3_3 import FRAHST_V3_3
from Frahst_v3_4 import FRAHST_V3_4
from Frahst_v4_0 import FRAHST_V4_0
from load_syn_ping_data import load_n_store
from QR_eig_solve import QRsolve_eigV
def FRAHST_V6_0(data, r=1, alpha=0.96, EW_alpha = 0.1, e_low = 0.96, e_high = 0.98, fix_init_Q = 0, holdOffTime=0,
evalMetrics = 'F', static_r = 0, r_upper_bound = None, L = 5, ignoreUp2 = 0,
data_norm_window = 50):
"""
Fast Rank Adaptive Householder Subspace Tracking Algorithm (FRAHST)
Version 6.0 - Different rank adjusting mechanism
compares sum of r eigenvalues to variance of entire data.
"r_hist": k_hist, # history of r values
"W_hist": W_hist, # history of Weights
"anomalies": anomalies,
}
res.update(res2)
return res
if __name__ == "__main__":
# A1 = np.mat(np.sin(np.arange(1,1001)/20.0)).T
# A11 = A1 * npm.rand((1,10))
# plt.figure()
# plt.plot(A11)
# a1 = np.sin(npm.arange(1,1001)/10.0)
# a2 = np.sin(npm.arange(1,1001)/50.0)
# A2 = np.mat([a1,a2]).T * npm.rand((2,10))
# plt.figure()
# plt.plot(A2)
data = load_data("chlorine")
data[1500:2000, 0:4] = 0.7
data = data[:, :15]
res = SPIRIT(data, 1, [0.8, 0.99])
res["Alg"] = "SPIRIT"
pltSummary2(res, data, (0.8, 0.99))
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 05 23:19:11 2011
THIS IS THE VERSION TO RUN ON MAC, NOT WINDOWS
@author: musselle
"""
from numpy import eye, zeros, dot, sqrt, log10, trace, arccos, nan, arange
import numpy as np
from numpy.random import rand
from numpy.linalg import qr, eig, norm, solve
from matplotlib.pyplot import plot, figure, title, step
from artSigs import genCosSignals_no_rand , genCosSignals
import scipy.io as sio
from utils import analysis, QRsolveA, pltSummary2
from PedrosFrahst import frahst_pedro_original
from Frahst_v3_1 import FRAHST_V3_1
from Frahst_v3_3 import FRAHST_V3_3
from load_syn_ping_data import load_n_store
def FRAHST_V3_4(data, r=1, alpha=0.96, e_low=0.96, e_high=0.98, fix_init_Q = 0, holdOffTime=0,
evalMetrics = 'F', static_r = 0, r_upper_bound = None, L = 5, ignoreUp2 = 0):
"""
Fast Rank Adaptive Householder Subspace Tracking Algorithm (FRAHST)
VErsion 3.4 - input data z is time lagged series up to length l.
- Algorithm is essentially same as 3.3, just adds pre processing to data vector
- input Vector z_t is now of length (N times L) where L is window length
- Q is increased accordingly
Version 3.3 - Add decay of S and in the event of vanishing inputs
- Make sure rank of S does not drop (and work out what that means!) - stops S going singular
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 05 23:19:11 2011
THIS IS THE VERSION TO RUN ON MAC, NOT WINDOWS
@author: musselle
"""
from numpy import eye, zeros, dot, sqrt, log10, trace, arccos, nan, arange
import numpy as np
import numpy.random as npr
import scipy as sp
from numpy.random import rand
from numpy.linalg import qr, eig, norm, solve
from matplotlib.pyplot import plot, figure, title, step
from artSigs import genCosSignals_no_rand, genCosSignals
import scipy.io as sio
from utils import analysis, QRsolveA, pltSummary2
from PedrosFrahst import frahst_pedro_original
from Frahst_v3_1 import FRAHST_V3_1
from load_syn_ping_data import load_n_store
def FRAHST_V3_3(data,
r=1,
alpha=0.96,
e_low=0.96,
e_high=0.98,
fix_init_Q=0,
holdOffTime=0,
evalMetrics='F',
static_r=0,
r_upper_bound=None,
