#!/usr/bin/python

import numpy as np    
import sys
import matplotlib.pyplot as plt



## height, weight, shoe size
X = np.array([
    [6, 180, 12],
    [5.92, 190, 11],
    [5.58, 170, 12],
    [5.92, 165, 10],
    [5, 100, 6],
    [5.5, 150, 8],
    [5.42, 130, 7],
    [5.75, 150, 9]])
## 1 = male, 2 = female
Y = np.array([1, 1, 1, 1, 2, 2, 2, 2])
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(X, Y)
print(clf.predict([[6, 130, 8]]))

## the posterior probabilities: [[posterior (male), posterior (female)]]
print (clf.predict_proba([[6, 130, 8]]))

print(clf.predict([[5, 180, 11]]))
print(clf.predict_proba([[5, 180, 11]]))

print(clf.predict([[5.3, 180, 11]]))
print(clf.predict_proba([[5.3, 180, 11]]))


## Incremental fit on a batch of samples.

## This method is expected to be called several times consecutively on
## different chunks of a dataset so as to implement out-of-core or
## online learning. 

#clf_pf = GaussianNB()
#clf_pf.partial_fit(X, Y, np.unique(Y))
#print(clf_pf.predict([[-0.8, -1]]))

XX = [n[0] for n in X]
YY = [n[1] for n in X]
ZZ = [n[2] for n in X]


#plt.plot(X, color="blue")
plt.scatter(XX, YY)
plt.ylabel('some points')
plt.savefig("gender.pdf")
# plt.show()



colors = ["g", "b", "r"]
for ii, pp in enumerate(X):
    plt.scatter(X[ii][0], X[ii][1], color = colors[Y[ii]])

plt.savefig("gender2.pdf")

for x in dir(clf):
    print (x, getattr(clf, x))

'''
note theta_ is the mean
  sigma_ is the variance - but which variance?
'''



#########################################################


from matplotlib import pyplot
import pylab
from mpl_toolkits.mplot3d import Axes3D
import random


fig = pylab.figure()
ax = Axes3D(fig)

'''
sequence_containing_x_vals = range(0,100)
sequence_containing_y_vals = range(0,100)
sequence_containing_z_vals = range(0,100)

random.shuffle(sequence_containing_x_vals)
random.shuffle(sequence_containing_y_vals)
random.shuffle(sequence_containing_z_vals)
'''

## ax.scatter(sequence_containing_x_vals, sequence_containing_y_vals, sequence_containing_z_vals)
ax.scatter(XX, YY, ZZ)
plt.savefig("gender3.pdf")


fig = pylab.figure()
ax = Axes3D(fig)

colors = ["g", "b", "r"]
for ii, pp in enumerate(XX):
    ax.scatter(XX[ii], YY[ii], ZZ[ii], color = colors[Y[ii]])
plt.savefig("gender4.pdf")

# pyplot.show()