class_vis.py

#!/usr/bin/python

#from udacityplots import *
import warnings
warnings.filterwarnings("ignore")

import matplotlib 
matplotlib.use('agg')

import matplotlib.pyplot as plt
import pylab as pl
import numpy as np

#import numpy as np
#import matplotlib.pyplot as plt
#plt.ioff()

def prettyPicture(clf, X_test, y_test):
 x_min = 0.0; x_max = 1.0
 y_min = 0.0; y_max = 1.0

 # Plot the decision boundary. For that, we will assign a color to each
 # point in the mesh [x_min, m_max]x[y_min, y_max].
 h = .01 # step size in the mesh
 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
 Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

 # Put the result into a color plot
 Z = Z.reshape(xx.shape)
 plt.xlim(xx.min(), xx.max())
 plt.ylim(yy.min(), yy.max())

 plt.pcolormesh(xx, yy, Z, cmap=pl.cm.seismic)

 # Plot also the test points
 grade_sig = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==0]
 bumpy_sig = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==0]
 grade_bkg = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==1]
 bumpy_bkg = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==1]

 plt.scatter(grade_sig, bumpy_sig, color = "b", label="fast")
 plt.scatter(grade_bkg, bumpy_bkg, color = "r", label="slow")
 plt.legend()
 plt.xlabel("bumpiness")
 plt.ylabel("grade")

 plt.savefig("test.png")
 
import base64
import json
import subprocess

def output_image(name, format, bytes):
 image_start = "BEGIN_IMAGE_f9825uweof8jw9fj4r8"
 image_end = "END_IMAGE_0238jfw08fjsiufhw8frs"
 data = {}
 data['name'] = name
 data['format'] = format
 data['bytes'] = base64.encodestring(bytes)
 print image_start+json.dumps(data)+image_end