Replace libLab3_Lidar.py

Labs/Lab_3_LiDAR_ML_Segmentation/libLab3_Lidar.py

@@ -418,13 +418,15 @@ def plot_contours_compare(X, y, X_train , y_train, X_test, y_test, classifiers,
 ###
 
 
-def plot_contours(X, y, classifier, resolution=0.02):
+def plot_contours(X, y, classifier, resolution=0.02,level=50):
 	"""
 	Function to plot single classifier contours
 	----
 	INPUT:
-		@X: Numpy array with data
+		@X: Numpy array with data, e.g. X.to_numpy()
 		@y: Numpy array with labels
+		@resolution: resolution of the contour plot
+		@level: depth of the intersection of hyperplanes
 		
 	----
 	OUTPUT:
@@ -445,26 +447,48 @@ def plot_contours(X, y, classifier, resolution=0.02):
 	from matplotlib import colors
 
 	# setup marker generator and color map
-	markers = ('s', 'x', 'o', '^', 'v')
-	colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
-	cmap = ListedColormap(colors[:len(np.unique(y))])
-	C = X[:, 0:2]
-
-    # plot the decision surface
-	x1_min, x1_max = C[:, 0].min() - 1, C[:, 0].max() + 1
-	x2_min, x2_max = C[:, 1].min() - 1, C[:, 1].max() + 1
-	xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
-						   np.arange(x2_min, x2_max, resolution))
-	Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
-	Z = Z.reshape(xx1.shape)
-	plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
-	plt.xlim(xx1.min(), xx1.max())
-	plt.ylim(xx2.min(), xx2.max())
-
-	for idx, cl in enumerate(np.unique(y)):
-		plt.scatter(x=C[y == cl, 0], y=C[y == cl, 1],
-					alpha=0.8, c=cmap(idx),
-					marker=markers[idx], label=cl)
+	# markers = ('s', 'x', 'o', '^', 'v')
+	# colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
+	# cmap = ListedColormap(colors[:len(np.unique(y))])
+	# C = X[:, 0:2]
+
+    # # plot the decision surface
+	# x1_min, x1_max = C[:, 0].min() - 1, C[:, 0].max() + 1
+	# x2_min, x2_max = C[:, 1].min() - 1, C[:, 1].max() + 1
+	# xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
+	# 					   np.arange(x2_min, x2_max, resolution))
+	# Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
+	# Z = Z.reshape(xx1.shape)
+	# plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
+	# plt.xlim(xx1.min(), xx1.max())
+	# plt.ylim(xx2.min(), xx2.max())
+
+	# for idx, cl in enumerate(np.unique(y)):
+	# 	plt.scatter(x=C[y == cl, 0], y=C[y == cl, 1],
+	# 				alpha=0.8, c=cmap(idx),
+	# 				marker=markers[idx], label=cl)
+
+	x_values = np.linspace(0, 1, np.int32(1/resolution))
+	x_mesh = np.meshgrid(x_values, x_values)
+	x_plane = np.array([x.ravel() for x in x_mesh])
+	x_projection = level * X[:, 3].mean()   * x_plane
+	x_input = np.vstack((x_plane, x_projection))
+	y_boundary = classifier.predict(x_input.T)
+	plt.figure()
+	plt.contourf(
+    	x_mesh[0],
+    	x_mesh[1],
+    	y_boundary.reshape(x_mesh[0].shape),
+    	levels=[0.5, 1.5, 2.5],
+    	zorder=0,
+		)
+	plt.scatter(
+    	X[:, 0],
+    	X[:, 1],
+    	c=y,
+    	cmap="tab20",
+    	zorder=2,
+	)		
 
 
 def plot_3dcladd(dx,dy,dz,y,density=1):
@@ -516,5 +540,4 @@ def plot_confMat(cnf_matrix,ClaName):
 	plt.title("Confusion matrix using "+ClaName, y=1.1);
 	plt.ylabel('Actual label');
 	plt.xlabel('Predicted label');
-	plt.show()
-
+	plt.show()
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