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'], data['subspace_error'],
['Input Data','Hidden\nVariables', 'Orthogonality\nError (dB)','Subspace\nError (dB)'],
'Time Steps', Title)
# Have such a big effect?????
# plot_4x1(streams, data['hidden'], data['orthog_error'],
# data['subspace_error'],
# ['Input Data','Hidden\nVariables',
# 'Orthogonality\nError (dB)','Subspace\nError (dB)'] , 'Time Steps', Title)
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]
#c = np.atleast_1d(b[::2])
#means = [0.0]*(len(c)/N)
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'],
data['subspace_error'], [
'Input Data', 'Hidden\nVariables',
'Orthogonality\nError (dB)', 'Subspace\nError (dB)'
], 'Time Steps', Title)
# Have such a big effect?????
# plot_4x1(streams, data['hidden'], data['orthog_error'],
# data['subspace_error'],
from pylab import *
from plot_utils import plot_4x1
# Must Define
# data2plot
# EH
# EL
ylims = (4, 0.92, 1.0)
# Title = r'$SNR = -9$'
plot_4x1(streams,
results[0]['data']['hidden'],
results[0]['data']['r_hist'],
results[0]['data']['e_ratio'],
['Input Data', 'Hidden\nVariables', 'Rank', 'Energy\nRatio'],
'Time steps',
ylims=ylims)
fig = gcf()
# dashed lines
fig.axes[0].axvline(x=300, ls='--', c='k')
fig.axes[0].axvline(x=600, ls='--', c='k')
fig.axes[1].axvline(x=300, ls='--', c='k')
fig.axes[1].axvline(x=600, ls='--', c='k')
fig.axes[2].axvline(x=300, ls='--', c='k')
fig.axes[2].axvline(x=600, ls='--', c='k')
fig.axes[3].axvline(x=300, ls='--', c='k')
fig.axes[3].axvline(x=600, ls='--', c='k')
def plot_res(self, var, xname="time steps", ynames=None, title=None, hline=1, anom=1):
if ynames is None:
ynames = [""] * 4
if title is None:
title = self.p["version"]
# Preprocessing
if "exp_ht" in var:
res["exp_ht"][res["exp_ht"] == 0.0] = np.nan
if "S" in self.A_version:
thresh = self.p["t_thresh"]
elif "T" in self.A_version:
thresh = self.p["x_thresh"]
elif "eng" in self.A_version:
thresh = (self.p["e_high"] - self.p["e_low"]) / 2
num_plots = len(var)
for i, v in enumerate(var):
if type(v) == str:
var[i] = getattr(self, "res")[v]
if num_plots == 1:
plt.figure()
plt.plot(var[0])
plt.title(title)
if anom == 1:
for x in self.res["anomalies"]:
plt.axvline(x, ymin=0.25, color="r")
elif num_plots == 2:
plot_2x1(var[0], var[1], ynames[:2], xname, main_title=title)
if hline == 1:
plt.hlines(-thresh, 0, self.res["ht"].shape[0], linestyles="dashed")
plt.hlines(+thresh, 0, self.res["ht"].shape[0], linestyles="dashed")
plt.ylim(-2 * thresh, 2 * thresh)
if anom == 1:
f = plt.gcf()
for ax in f.axes[:-1]:
for x in self.res["anomalies"]:
ax.axvline(x, ymin=0.25, color="r")
elif num_plots == 3:
plot_3x1(var[0], var[1], var[2], ynames[:3], xname, main_title=title)
if hline == 1:
plt.hlines(-thresh, 0, self.res["ht"].shape[0], linestyles="dashed")
plt.hlines(+thresh, 0, self.res["ht"].shape[0], linestyles="dashed")
plt.ylim(-2 * thresh, 2 * thresh)
if anom == 1:
f = plt.gcf()
for ax in f.axes[:-1]:
for x in self.res["anomalies"]:
ax.axvline(x, ymin=0.25, color="r")
elif num_plots == 4:
plot_4x1(var[0], var[1], var[2], var[3], ynames[:4], xname, main_title=title)
plt.title(title)
if hline == 1:
plt.hlines(-thresh, 0, self.res["ht"].shape[0], linestyles="dashed")
plt.hlines(+thresh, 0, self.res["ht"].shape[0], linestyles="dashed")
plt.ylim(-2 * thresh, 2 * thresh)
if anom == 1:
f = plt.gcf()
for ax in f.axes[:-1]:
for x in self.res["anomalies"]:
ax.axvline(x, ymin=0.25, color="r")
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]
#c = np.atleast_1d(b[::2])
#means = [0.0]*(len(c)/N)
def plot_res(self, var, xname = 'Time Steps', ynames = None, title = None, hline= 1, anom = 1):
"""Plots each of the elements given in var.
var = list of variables. Maximum = 4. if string, will look for them in self.res structure
hline = whether to plot threshold values on final plot.
anom = whether to plot anomalous time ticks.
"""
if ynames is None:
ynames = ['']*4
if title is None:
title = (self.p['version'])
if 'SRE' in self.A_version:
thresh = self.p['t_thresh']
num_plots = len(var)
for i, v in enumerate(var):
if type(v) == str :
var[i] = getattr(self, 'res')[v]
if num_plots == 1:
plt.figure()
plt.plot(var[0])
plt.title(title)
if anom == 1:
for x in self.res['anomalies']:
plt.axvline(x, ymin=0.9, color='r')
elif num_plots == 2:
plot_2x1(var[0], var[1], ynames[:2], xname, main_title = title)
if hline == 1:
plt.hlines(-thresh, 0, self.res['ht'].shape[0], linestyles = 'dashed')
plt.hlines(+thresh, 0, self.res['ht'].shape[0], linestyles = 'dashed')
plt.ylim(-3*thresh,3*thresh)
if anom == 1:
f = plt.gcf()
for ax in f.axes[:-1]:
for x in self.res['anomalies']:
ax.axvline(x, ymin=0.9, color='r')
elif num_plots == 3:
plot_3x1(var[0], var[1], var[2], ynames[:3] , xname, main_title = title)
if hline == 1:
plt.hlines(-thresh, 0, self.res['ht'].shape[0], linestyles = 'dashed')
plt.hlines(+thresh, 0, self.res['ht'].shape[0], linestyles = 'dashed')
plt.ylim(-3*thresh,3*thresh)
if anom == 1:
f = plt.gcf()
for ax in f.axes[:-1]:
for x in self.res['anomalies']:
ax.axvline(x, ymin=0.9, color='r')
elif num_plots == 4:
plot_4x1(var[0], var[1], var[2], var[3], ynames[:4], xname, main_title = title)
plt.title(title)
if hline == 1:
plt.hlines(-thresh, 0, self.res['ht'].shape[0], linestyles = 'dashed')
plt.hlines(+thresh, 0, self.res['ht'].shape[0], linestyles = 'dashed')
plt.ylim(-3*thresh,3*thresh)
if anom == 1:
f = plt.gcf()
for ax in f.axes[:-1]:
for x in self.res['anomalies']:
ax.axvline(x, ymin=0.9, color='r')
plt.draw()
from pylab import *
from plot_utils import plot_4x1
# Must Define
# data2plot
# EH
# EL
ylims = (4,0.92,1.0)
# Title = r'$SNR = -9$'
plot_4x1(streams, results[0]['data']['hidden'], results[0]['data']['r_hist'], results[0]['data']['e_ratio'],
['Input Data', 'Hidden\nVariables', 'Rank', 'Energy\nRatio'], 'Time steps', ylims = ylims)
fig = gcf()
# dashed lines
fig.axes[0].axvline(x = 300, ls = '--', c = 'k')
fig.axes[0].axvline(x = 600, ls = '--', c = 'k')
fig.axes[1].axvline(x = 300, ls = '--', c = 'k')
fig.axes[1].axvline(x = 600, ls = '--', c = 'k')
fig.axes[2].axvline(x = 300, ls = '--', c = 'k')
fig.axes[2].axvline(x = 600, ls = '--', c = 'k')
fig.axes[3].axvline(x = 300, ls = '--', c = 'k')
fig.axes[3].axvline(x = 600, ls = '--', c = 'k')
fig.axes[3].axhline(y=EH, ls = '--')
fig.axes[3].axhline(y=EL, ls = '--')
