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plotting wolf/cow fence boundaries
o o o
o x
o x x x
x o
x x x o
o o
import numpy as np
import pylab as pl
from sklearn import svm
from sklearn import linear_model
from sklearn import tree
import pandas as pd
def plot_results_with_hyperplane(clf, clf_name, df, plt_nmbr):
x_min, x_max = df.x.min() - .5, df.x.max() + .5
y_min, y_max = df.y.min() - .5, df.y.max() + .5
# step between points. i.e. [0, 0.02, 0.04, ...]
step = .02
# to plot the boundary, we're going to create a matrix of every possible point
# then label each point as a wolf or cow using our classifier
xx, yy = np.meshgrid(np.arange(x_min, x_max, step), np.arange(y_min, y_max, step))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# this gets our predictions back into a matrix
Z = Z.reshape(xx.shape)
# create a subplot (we're going to have more than 1 plot on a given image)
pl.subplot(2, 2, plt_nmbr)
# plot the boundaries
pl.pcolormesh(xx, yy, Z,
# plot the wolves and cows
for animal in df.animal.unique():
label="cows" if animal=="x" else "wolves",
data = open("cows_and_wolves.txt").read()
data = [row.split('\t') for row in data.strip().split('\n')]
animals = []
for y, row in enumerate(data):
for x, item in enumerate(row):
# x's are cows, o's are wolves
if item in ['o', 'x']:
animals.append([x, y, item])
df = pd.DataFrame(animals, columns=["x", "y", "animal"])
df['animal_type'] = df.animal.apply(lambda x: 0 if x=="x" else 1)
# train using the x and y position coordiantes
train_cols = ["x", "y"]
clfs = {
"SVM": svm.SVC(),
"Logistic" : linear_model.LogisticRegression(),
"Decision Tree": tree.DecisionTreeClassifier(),
plt_nmbr = 1
for clf_name, clf in clfs.iteritems():[train_cols], df.animal_type)
plot_results_with_hyperplane(clf, clf_name, df, plt_nmbr)
plt_nmbr += 1
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