fixed indentation issue in knnplots.py

Author: chihchunchen

knnplots.py

@@ -4,58 +4,49 @@
 import numpy as np
 
 def plotvector(XTrain, yTrain, XTest, yTest, weights, upperLim = 310):
-results = []
-for n in range(1, upperLim, 4):
- clf = neighbors.KNeighborsClassifier(n_neighbors = n, weights = weights)
- clf = clf.fit(XTrain, yTrain)
- preds = clf.predict(XTest)
- accuracy = clf.score(XTest, yTest)
- results.append([n, accuracy])
- 
-results = np.array(results)
-return(results)
+ results = []
+ for n in range(1, upperLim, 4):
+ clf = neighbors.KNeighborsClassifier(n_neighbors = n, weights = weights)
+ clf = clf.fit(XTrain, yTrain)
+ preds = clf.predict(XTest)
+ accuracy = clf.score(XTest, yTest)
+ results.append([n, accuracy])
+ results = np.array(results)
+ return(results)
 
 def plotaccuracy(XTrain, yTrain, XTest, yTest, upperLim):
-pltvector1 = plotvector(XTrain, yTrain, XTest, yTest, weights = "uniform")
-pltvector2 = plotvector(XTrain, yTrain, XTest, yTest, weights = "distance")
-line1 = plt.plot(pltvector1[:,0], pltvector1[:,1], label = "uniform")
-line2 = plt.plot(pltvector2[:,0], pltvector2[:,1], label = "distance")
-plt.legend(loc=3)
-plt.ylim(0.5, 1)
-plt.title("Accuracy with Increasing K")
-plt.show()
+ pltvector1 = plotvector(XTrain, yTrain, XTest, yTest, weights = "uniform")
+ pltvector2 = plotvector(XTrain, yTrain, XTest, yTest, weights = "distance")
+ line1 = plt.plot(pltvector1[:,0], pltvector1[:,1], label = "uniform")
+ line2 = plt.plot(pltvector2[:,0], pltvector2[:,1], label = "distance")
+ plt.legend(loc=3)
+ plt.ylim(0.5, 1)
+ plt.title("Accuracy with Increasing K")
+ plt.show()
 
 
 
 def decisionplot(XTrain, yTrain, n_neighbors, weights):
-h = .02 # step size in the mesh
- 
-Xtrain = XTrain[:, :2] # we only take the first two features.
- 
-# Create color maps
-cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])
-cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])
-
-clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
-clf.fit(Xtrain, yTrain)
-
-# Plot the decision boundary. For that, we will assign a color to each
-# point in the mesh [x_min, m_max]x[y_min, y_max].
-x_min, x_max = Xtrain[:, 0].min() - 1, Xtrain[:, 0].max() + 1
-y_min, y_max = Xtrain[:, 1].min() - 1, Xtrain[:, 1].max() + 1
-xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
- np.arange(y_min, y_max, h))
-Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
-
-# Put the result into a color plot
-Z = Z.reshape(xx.shape)
-plt.figure()
-plt.pcolormesh(xx, yy, Z, cmap = cmap_light)
-
-# Plot also the training points
-plt.scatter(Xtrain[:, 0], Xtrain[:, 1], c = yTrain, cmap = cmap_bold)
-plt.xlim(xx.min(), xx.max())
-plt.ylim(yy.min(), yy.max())
-plt.title("2-Class classification (k = %i, weights = '%s')"
- % (n_neighbors, weights))
-plt.show()
+ h = .02 # step size in the mesh
+ Xtrain = XTrain[:, :2] # we only take the first two features.
+ # Create color maps
+ cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])
+ cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])
+ clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
+ clf.fit(Xtrain, yTrain)
+ # Plot the decision boundary. For that, we will assign a color to each
+ # point in the mesh [x_min, m_max]x[y_min, y_max].
+ x_min, x_max = Xtrain[:, 0].min() - 1, Xtrain[:, 0].max() + 1
+ y_min, y_max = Xtrain[:, 1].min() - 1, Xtrain[:, 1].max() + 1
+ xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
+ Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+ # Put the result into a color plot
+ Z = Z.reshape(xx.shape)
+ plt.figure()
+ plt.pcolormesh(xx, yy, Z, cmap = cmap_light)
+ # Plot also the training points
+ plt.scatter(Xtrain[:, 0], Xtrain[:, 1], c = yTrain, cmap = cmap_bold)
+ plt.xlim(xx.min(), xx.max())
+ plt.ylim(yy.min(), yy.max())
+ plt.title("2-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights))
+ plt.show()