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inspection.py
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inspection.py
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'''
$ python --version
Python 2.7.15
$ pip list
Package Version
----------------------------- ----------
absl-py 0.6.1
astor 0.7.1
backports.functools-lru-cache 1.5
backports.weakref 1.0.post1
certifi 2018.11.29
cycler 0.10.0
enum34 1.1.6
funcsigs 1.0.2
futures 3.2.0
gast 0.2.0
grpcio 1.16.1
h5py 2.8.0
joblib 0.13.0
Keras 2.2.4
Keras-Applications 1.0.6
Keras-Preprocessing 1.0.5
kiwisolver 1.0.1
Markdown 3.0.1
matplotlib 2.2.3
mock 2.0.0
mpmath 1.0.0
numpy 1.15.4
pandas 0.23.4
patsy 0.5.1
pbr 5.1.1
Pillow 5.3.0
pip 18.1
protobuf 3.6.1
pymc3 3.5
pyparsing 2.3.0
python-dateutil 2.7.5
pytz 2018.7
PyYAML 3.13
scipy 1.1.0
setuptools 39.0.1
six 1.11.0
subprocess32 3.5.3
sympy 1.3
tensorboard 1.12.0
tensorflow 1.12.0
termcolor 1.1.0
Theano 1.0.3
tqdm 4.28.1
Werkzeug 0.14.1
wheel 0.32.3
'''
import matplotlib.pyplot as plt
import matplotlib.cm as cmap
import numpy as np
np.random.seed(206)
import theano
import theano.tensor as tt
import pymc3 as pm
from sympy import diff
lengthscale = 0.1
eta = 2.0
cov = eta**2 * pm.gp.cov.ExpQuad(1, lengthscale)
X = np.linspace(-1, 1, 1500)[:,None]
K = cov(X).eval()
plt.plot(X, pm.MvNormal.dist(mu=np.zeros(K.shape[0]), cov=K).random(size=1).T, label="ro=0.1");
lengthscale = 1
eta = 2.0
cov = eta**2 * pm.gp.cov.ExpQuad(1, lengthscale)
X = np.linspace(-1, 1, 1500)[:,None]
K = cov(X).eval()
plt.plot(X, pm.MvNormal.dist(mu=np.zeros(K.shape[0]), cov=K).random(size=1).T, label="ro=1");
lengthscale = 10
eta = 2.0
cov = eta**2 * pm.gp.cov.ExpQuad(1, lengthscale)
X = np.linspace(-1, 1, 1500)[:,None]
K = cov(X).eval()
plt.plot(X, pm.MvNormal.dist(mu=np.zeros(K.shape[0]), cov=K).random(size=1).T, label="ro=10");
plt.ylabel("Y");
plt.xlabel("X");
plt.legend(bbox_to_anchor=(0.6, 1), loc=2, borderaxespad=0.)
plt.savefig('testAccuracyVersusNumOfEpochs.png')
#********************************************************************************************
plt.clean()
lengthscale = 0.1
eta = 2.0
cov = eta**2 * pm.gp.cov.ExpQuad(1, lengthscale)
print(type(cov))
X = np.linspace(-1, 1, 1500)[:,None]
K = cov(X).eval()
plt.plot(X, pm.MvNormal.dist(mu=np.zeros(K.shape[0]), cov=K).random(size=1).T, label="ro=0.1");
lengthscale = 1
eta = 2.0
cov = eta**2 * pm.gp.cov.ExpQuad(1, lengthscale)
X = np.linspace(-1, 1, 1500)[:,None]
K = cov(X).eval()
plt.plot(X, pm.MvNormal.dist(mu=np.zeros(K.shape[0]), cov=K).random(size=1).T, label="ro=1");
lengthscale = 10
eta = 2.0
cov = eta**2 * pm.gp.cov.ExpQuad(1, lengthscale)
X = np.linspace(-1, 1, 1500)[:,None]
K = cov(X).eval()
plt.plot(X, pm.MvNormal.dist(mu=np.zeros(K.shape[0]), cov=K).random(size=1).T, label="ro=10");
plt.ylabel("Y");
plt.xlabel("X");
plt.legend(bbox_to_anchor=(0.6, 1), loc=2, borderaxespad=0.)
plt.savefig('trainAccuracyVersusNumOfEpochs.png')