def train_plot(features, labels):
y0, y1 = features[:, 2].min() * .9, features[:, 2].max() * 1.1
x0, x1 = features[:, 0].min() * .9, features[:, 0].max() * 1.1
X = np.linspace(x0, x1, 100)
Y = np.linspace(y0, y1, 100)
X, Y = np.meshgrid(X, Y)
model = learn_model(1, features[:, (0, 2)], np.array(labels))
C = apply_model(np.vstack([X.ravel(), Y.ravel()]).T,
model).reshape(X.shape)
if COLOUR_FIGURE:
cmap = ListedColormap([(1., .6, .6), (.6, 1., .6), (.6, .6, 1.)])
else:
cmap = ListedColormap([(1., 1., 1.), (.2, .2, .2), (.6, .6, .6)])
plt.xlim(x0, x1)
plt.ylim(y0, y1)
plt.xlabel(feature_names[0])
plt.ylabel(feature_names[2])
plt.pcolormesh(X, Y, C, cmap=cmap)
if COLOUR_FIGURE:
cmap = ListedColormap([(1., .0, .0), (.0, 1., .0), (.0, .0, 1.)])
plt.scatter(features[:, 0], features[:, 2], c=labels, cmap=cmap)
else:
for lab, ma in zip(range(3), "Do^"):
plt.plot(features[labels == lab, 0],
features[labels == lab, 2],
ma,
c=(1., 1., 1.))
def train_plot(features, labels):
y0, y1 = features[:, 2].min() * .9, features[:, 2].max() * 1.1
x0, x1 = features[:, 0].min() * .9, features[:, 0].max() * 1.1
X = np.linspace(x0, x1, 100)
Y = np.linspace(y0, y1, 100)
X, Y = np.meshgrid(X, Y)
model = learn_model(1, features[:, (0, 2)], np.array(labels))
C = apply_model(
np.vstack([X.ravel(), Y.ravel()]).T, model).reshape(X.shape)
if COLOUR_FIGURE:
cmap = ListedColormap([(1., .6, .6), (.6, 1., .6), (.6, .6, 1.)])
else:
cmap = ListedColormap([(1., 1., 1.), (.2, .2, .2), (.6, .6, .6)])
plt.xlim(x0, x1)
plt.ylim(y0, y1)
plt.xlabel(feature_names[0])
plt.ylabel(feature_names[2])
plt.pcolormesh(X, Y, C, cmap=cmap)
if COLOUR_FIGURE:
cmap = ListedColormap([(1., .0, .0), (.0, 1., .0), (.0, .0, 1.)])
plt.scatter(features[:, 0], features[:, 2], c=labels, cmap=cmap)
else:
for lab, ma in zip(range(3), "Do^"):
plt.plot(features[labels == lab, 0], features[
labels == lab, 2], ma, c=(1., 1., 1.))
def train_plot(features, labels):
y0, y1 = features[:, 2].min()*.9, features[:,2].max()*1.1
x0, x1 = features[:, 0].min()*.9, features[:,0].max()*1.1
X=np.linspace(x0, x1, 100)
Y=np.linspace(y0, y1, 100)
X, Y = np.meshgrid(X, Y)
#meshgird is very useful to evaluate functions on a grid
model = learn_model(1, features[:, (0,2)], np.array(labels))
C = apply_model(
np.vstack([X.ravel(), Y.ravel()]).T, model).reshape(X.shape)
if COLOUR_FIGURE:
cmap = ListedColormap([(1., .6, .6), (.6, 1., .6), (.6, .6, 1.)])
else:
cmap = ListedColormap([(1., 1, 1), (.2, .2, .2), (.6, .6, .6)])
plt.xlim(x0,x1)
plt.ylim(y0,y1)
plt.xlabel(feature_names[0])
plt.ylabel(feature_names[2])
plt.pcolormesh(X,Y,C, cmap=cmap)
if COLOUR_FIGURE:
#cmap = ListedColormap([(1., .0, .0), (.0, 1, .0), (.0, .0, 1.)])
#plt.scatter(features[:,0], features[:,2], c=labels, cmap=cmap)
# this is a better way to plot instead of using the for .
col=[(1., .0, .0,), (.0, 1, .0), (.0, .0, 1.)]
for lab, c in zip(range(3), col):
plt.scatter(features[labels==lab,0],
features[labels==lab, 2], c=c)
else:
for lab, ma in zip(range(3), "Do^"):
plt.plot(features[labels == lab, 0], features[
labels == lab, 2], ma, c=(1.,1.,1.))
def cross_validate(features, labels):
error = 0.0
for fold in range(10):
training = np.ones(len(features), bool)
training[fold::10] = 0
testing = ~training
model = learn_model(1, features[training], labels[training])
test_error = accuracy(features[testing], labels[testing], model)
error += test_error
return error / 10.0
def cross_validation(features, labels):
error = 0.0
for fold in range(10):
training = np.ones(len(features), bool)
training[fold::10] = 0
testing = ~training
model = learn_model(1, features[training], labels[training])
test_error = accuracy(features[testing], labels[testing], model)
error += test_error
return error / 10.0
