This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# -*- coding: utf-8 -*- | |
#from IPython import get_ipython | |
#get_ipython().magic('reset -sf') | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import json | |
############################################################################## | |
def matrixpush(m, row): | |
if not np.all(np.isfinite(values[:,0])): | |
i = np.count_nonzero(np.logical_not(np.isnan(values[:,0]))) | |
m[i,] = row | |
else: | |
m = np.vstack([m,row]) | |
m = m[1:,] | |
return(m) | |
def getobs( x, max = 5, err=0 ): | |
if (err==0): | |
obsr = -1*pow((x-max),2) | |
else: | |
obsr = -1*pow((x-max),2) + np.random.normal(0,err,1) | |
return obsr; | |
############################################################################## | |
stream = 200 # Length of stream | |
inttime = 100 # Integration time | |
amplitude = 1.4 # Amlitude LIF | |
learnrate = .004 # Learnrate | |
omega = 0.8 # Omega | |
x0 = 1.0 # Startvalue | |
############################################################################## | |
p_return = 0.80 # Chance that returns | |
variance = 1 # variance in observations | |
############################################################################## | |
values = np.zeros((inttime,3)) | |
values.fill(np.nan) | |
track_x0 = [] | |
track_x = [] | |
track_t = [] | |
track_y = [] | |
x = 0.0 | |
t = 0.0 | |
y = 0.0 | |
############################################################################## | |
for i in range(0,stream): | |
t = i+1 | |
x = x0 + amplitude*np.cos(omega * t) | |
if np.all(np.isfinite(values[:,0])): | |
x0 = np.mean(values[:,1]) | |
x0 = x0 + learnrate * sum( values[:,2] ) | |
#values.fill(np.nan) | |
if np.random.binomial(1, p_return, 1)==1: | |
y = amplitude*np.cos(omega * t)*getobs(x,5,variance) | |
row_to_add = np.array([t,x,y]) | |
values = matrixpush(values, row_to_add) | |
track_t = np.append(track_t, t) | |
track_x = np.append(track_x, x) | |
track_y = np.append(track_y, y) | |
track_x0 = np.append(track_x0, x0) | |
############################################################################## | |
# plot some vars | |
plt.plot(track_x) | |
plt.show() | |
plt.plot(track_x0) | |
plt.show() | |
# print final x0 | |
print(x0) | |
############################################################################## | |
def _np_nan_fill(rows,columns): | |
nan_values = np.zeros((rows,columns)) | |
nan_values.fill(np.nan) | |
nan_values = json.dumps(nan_values.tolist()) | |
return nan_values |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment