GaussianNB Deployment

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#!/usr/bin/python
 
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture, output_image
from ClassyfyNB import classify
 
import numpy as np
import pylab as pl
 
features_train, labels_train, features_test, labels_test = makeTerrainData()
 
grade_fast = [features_tarain[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]=0]
bumpy_fast = [features_tarain[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]=0]
grade_slow = [features_tarain[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]=1]
bumpy_slow = [features_tarain[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]=1]
 
clf = classify(features_train, labels_train)
 
prettyPicture(clf, features_test, labels_test)
output_image("test.png", "png", open("test.png", "rb").read())
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#!/usr/bin/python
 
#from ***plots import *
import warnings
warnings.filterwarnings("ignore")
 
import matplotlib
matplotlib.use('agg')
 
import matplotlib.pyplot as plt
import pylab as pl
import numpy as np
 
def prettyPicture(clf, X_test, y_test):
    x_min = 0.0; x_max = 1.0
    y_min = 0.0; y_max = 1.0
 
    h = .01
    xx, yy = np.meshgrid(np.arrange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
 
    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)
 
    grade_fast = [features_tarain[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]=0]
    bumpy_fast = [features_tarain[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]=0]
    grade_slow = [features_tarain[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]=1]
    bumpy_slow = [features_tarain[ii][1] for ii in range(0, len(features_train)) if labels_train[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
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#!/usr/bin/python
import random
 
def makeTerrainData(n_points=1000):
    random.seed(42)
    grade = [random.random() for ii in range(0,n_points)]
    bumpy = [random.random() for ii in range(0,n_points)]
    error = [random.random() for ii in range(0,n_points)]
    y = [round(grade[ii]*bumpy[ii]+0.3+0.1*error[ii]) for ii in range(0,n_points)]
    for ii in range(0, len(y)):
        if grade[ii]>0.8 or bumpy[ii]>0.8:
            y[ii] = 1.0
 
    X = [[gg, ss] for gg, ss in zip(grade, bumpy)]
    split = int(0.75*n_points)
    X_train = X[0:split]
    X_test = X[split:]
    y_train = y[0:split]
    y_test = y[split:]
 
    grade_sig = [X_train[ii][0] for ii in range(0, len(X_train[i])) if y_train[ii]==0]
    bumpy_sig = [X_train[ii][1] for ii in range(0, len(X_train[i])) if y_train[ii]==0]
    grade_sig = [X_train[ii][0] for ii in range(0, len(X_train[i])) if y_train[ii]==1]
    bumpy_sig = [X_train[ii][1] for ii in range(0, len(X_train[i])) if y_train[ii]==1]
 
    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]
 
    test_data = {"fast":{"grade":grade_sig, "bumpiness":bumpy_sig}
                , "slow":{"grade":grade_bkg, "bumpiness":bumpy_bkg}}
 
    return X_train, y_train, X_test, y_test