glamp · March 5, 2021 06:59 · Dec 26, 2012 · Dec 23, 2012
diff --git a/svmflag.py b/svmflag.py
@@ -48,7 +48,7 @@
 cols = ["x", "y"]
 
 clfs = {
-    "SVC": svm.SVC(degree=0.5),
+    "SVM": svm.SVC(degree=0.5),
     "Logistic" : linear_model.LogisticRegression(),
     "Decision Tree": tree.DecisionTreeClassifier()
 }

diff --git a/svmflag.py b/svmflag.py
@@ -0,0 +1,95 @@
+import numpy as np
+import pylab as pl
+import pandas as pd
+
+from sklearn import svm
+from sklearn import linear_model
+from sklearn import tree
+
+from sklearn.metrics import confusion_matrix
+
+x_min, x_max = 0, 15
+y_min, y_max = 0, 10
+step = .1
+# 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))
+
+df = pd.DataFrame(data={'x': xx.ravel(), 'y': yy.ravel()})
+
+df['color_gauge'] = (df.x-7.5)**2 + (df.y-5)**2
+df['color'] = df.color_gauge.apply(lambda x: "red" if x <= 15 else "green")
+df['color_as_int'] = df.color.apply(lambda x: 0 if x=="red" else 1)
+
+print "Points on flag:"
+print df.groupby('color').size()
+print
+
+figure = 1
+
+# plot a figure for the entire dataset
+for color in df.color.unique():
+    idx = df.color==color
+    pl.subplot(2, 2, figure)
+    pl.scatter(df[idx].x, df[idx].y, color=color)
+    pl.title('Actual')
+
+
+train_idx = df.x < 10
+
+train = df[train_idx]
+test = df[-train_idx]
+
+
+print "Training Set Size: %d" % len(train)
+print "Test Set Size: %d" % len(test)
+
+# train using the x and y position coordiantes
+cols = ["x", "y"]
+
+clfs = {
+    "SVC": svm.SVC(degree=0.5),
+    "Logistic" : linear_model.LogisticRegression(),
+    "Decision Tree": tree.DecisionTreeClassifier()
+}
+
+
+# racehorse different classifiers and plot the results
+for clf_name, clf in clfs.iteritems():
+    figure += 1
+
+    # train the classifier
+    clf.fit(train[cols], train.color_as_int)
+
+    # get the predicted values from the test set
+    test['predicted_color_as_int'] = clf.predict(test[cols])
+    test['pred_color'] = test.predicted_color_as_int.apply(lambda x: "red" if x==0 else "green")
+
+    # create a new subplot on the plot
+    pl.subplot(2, 2, figure)
+    # plot each predicted color
+    for color in test.pred_color.unique():
+        # plot only rows where pred_color is equal to color
+        idx = test.pred_color==color
+        pl.scatter(test[idx].x, test[idx].y, color=color)
+
+    # plot the training set as well
+    for color in train.color.unique():
+        idx = train.color==color
+        pl.scatter(train[idx].x, train[idx].y, color=color)
+
+    # add a dotted line to show the boundary between the training and test set
+    # (everything to the right of the line is in the test set)
+    #this plots a vertical line
+    train_line_y = np.linspace(y_min, y_max) #evenly spaced array from 0 to 10
+    train_line_x = np.repeat(10, len(train_line_y)) #repeat 10 (threshold for traininset) n times
+    # add a black, dotted line to the subplot
+    pl.plot(train_line_x, train_line_y, 'k--', color="black")
+
+    pl.title(clf_name)
+
+    print "Confusion Matrix for %s:" % clf_name
+    print confusion_matrix(test.color, test.pred_color)
+pl.show()
+
+