def test():
'''
Debug
'''
import load
import matplotlib.pyplot as plt
import os
feature_names, data, target_names, targets = load.load_seeds()
target_names = np.array(target_names)
labels = target_names[targets]
pred = apply_knn(data, data, targets)
pred = target_names[pred]
#label_names = np.unique(labels)
# plot original data
plt.subplot(1, 2, 1)
for k,marker,c in zip(xrange(3), "<ox", "rgb"):
plt.scatter( data[labels == target_names[k], 0],
data[labels == target_names[k], 2],
marker = marker,
c = c );
plt.title("origin")
# plot knn predicted data
plt.subplot(1, 2, 2)
for k,marker,c in zip(xrange(3), "<ox", "rgb"):
plt.scatter( data[pred == target_names[k], 0],
data[pred == target_names[k], 2],
marker = marker,
c = c)
# re-plot misclassified data
wrong = plt.scatter( data[pred != labels, 0],
data[pred != labels, 2],
marker = 's',
c = 'y' );
plt.legend([wrong], ["Wrong"], loc='upper right')
file_dir = os.path.dirname(os.path.realpath(__file__))
plt.savefig(os.path.join(file_dir, "test", "knn_test.png"))
print "Accuracy: %f" % accuracy(labels, pred)
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from load import load_seeds
from knn import apply_knn
feature_names, data, target_names, targets = load_seeds()
#feature_names, data, target, labels = load_seeds()
#label_names = np.unique(labels).tolist()
# Regularization
mean, std = data.mean(0), data.std(0)
data -= mean
data /= std
x0, x1 = data[:,0].min()*0.9, data[:,0].max()*1.1
y0, y1 = data[:,2].min()*0.9, data[:,2].max()*1.1
x = np.linspace(x0, x1, 100)
y = np.linspace(y0, y1, 100)
x,y = np.meshgrid(x, y)
new_examples = np.vstack([x.ravel(), y.ravel()]).T
print new_examples.shape
print data[:, (0,2)].shape
pred = apply_knn(new_examples,
data[:, (0,2)],
targets).reshape(x.shape)
