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 = fit_model(1, features[:, (0, 2)], np.array(labels))
C = predict(
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 plot_decision(features, labels):
'''KNN에 대한 결정선 그리기
Parameters
----------
features : ndarray
labels : sequence
Returns
-------
fig : Matplotlib Figure
ax : Matplotlib Axes
'''
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 = fit_model(1, features[:, (0, 2)], np.array(labels))
## 원본 소스 오류
# C = predict(
# np.vstack([X.ravel(), Y.ravel()]).T, model).reshape(X.shape)
C = predict(
model, np.vstack([X.ravel(), Y.ravel()]).T).reshape(X.shape)
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 = fit_model(1, features[:, (0, 2)], np.array(labels))
C = predict(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 oneFold():
# masks for training and testing
training = np.ones(len(features), bool)
# sample
training[1::4] = 0
testing = ~training
k = 1
model = fit_model(k, features[training], labels[training])
accr = accuracy(model, features[testing], labels[testing])
print 'Aprox Accuracy was{0:.1%}'.format(accr)
def cross_validate(features, labels):
'''Compute cross-validation errors'''
error = 0.0
for fold in range(10):
training = np.ones(len(features), bool)
training[fold::10] = 0
testing = ~training
model = fit_model(1, features[training], labels[training])
test_error = accuracy(features[testing], labels[testing], model)
error += test_error
return error / 10.0
def cross_validate(features, labels):
'''Compute cross-validation errors'''
error = 0.0
for fold in range(10):
training = np.ones(len(features), bool)
training[fold::10] = 0
testing = ~training
model = fit_model(1, features[training], labels[training])
test_error = accuracy(features[testing], labels[testing], model)
error += test_error
return error / 10.0
def cross_validate(features, labels):
k = 1
accr = 0.0
nFolds = 10
for fold in range(nFolds):
training = np.ones(len(features), bool)
# unsample every nFold
training[fold::nFolds] = 0
testing = ~training
model = fit_model(k, features[training], labels[training])
accr += accuracy(model, features[testing], labels[testing])
return accr / nFolds
def plot_decision(features, labels):
'''Plots decision boundary for KNN
Parameters
----------
features : ndarray
labels : sequence
Returns
-------
fig : Matplotlib Figure
ax : Matplotlib Axes
'''
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 = fit_model(1, features[:, (0, 2)], np.array(labels))
C = predict(model, np.vstack([X.ravel(), Y.ravel()]).T).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)])
fig, ax = plt.subplots()
ax.set_xlim(x0, x1)
ax.set_ylim(y0, y1)
ax.set_xlabel(feature_names[0])
ax.set_ylabel(feature_names[2])
ax.pcolormesh(X, Y, C, cmap=cmap)
if COLOUR_FIGURE:
cmap = ListedColormap([(1., .0, .0), (.0, 1., .0), (.0, .0, 1.)])
ax.scatter(features[:, 0], features[:, 2], c=labels, cmap=cmap)
else:
for lab, ma in zip(range(3), "Do^"):
ax.plot(features[labels == lab, 0],
features[labels == lab, 2],
ma,
c=(1., 1., 1.))
return fig, ax
def plot_decision(features, labels):
'''Plots decision boundary for KNN
Parameters
----------
features : ndarray
labels : sequence
Returns
-------
fig : Matplotlib Figure
ax : Matplotlib Axes
'''
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 = fit_model(1, features[:, (0, 2)], np.array(labels))
C = predict(
model, np.vstack([X.ravel(), Y.ravel()]).T).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)])
fig,ax = plt.subplots()
ax.set_xlim(x0, x1)
ax.set_ylim(y0, y1)
ax.set_xlabel(feature_names[0])
ax.set_ylabel(feature_names[2])
ax.pcolormesh(X, Y, C, cmap=cmap)
if COLOUR_FIGURE:
cmap = ListedColormap([(1., .0, .0), (.0, 1., .0), (.0, .0, 1.)])
ax.scatter(features[:, 0], features[:, 2], c=labels, cmap=cmap)
else:
for lab, ma in zip(range(3), "Do^"):
ax.plot(features[labels == lab, 0], features[
labels == lab, 2], ma, c=(1., 1., 1.))
return fig,ax
