[Course] Test script

Type : feat.

Abstract:
* Test script

ch02/perceptron.py

@@ -14,16 +14,14 @@ def __init__(self, eta=0.01, n_iter=50, random_state=1):
 
     def fit(self, X, y):
         rgen = np.random.RandomState(self.random_state)
-        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.Shape[1])
+        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])
         self.error_ = []
 
         for _ in range(self.n_iter):
             errors = 0
             zip_data = zip(X, y)
             for xi, target in zip_data:
-                print(f"{xi}, {target}")
                 update = self.eta * (target - self.predict(xi))
-                print(f"update {update}")
                 self.w_[1:] += update * xi
                 self.w_[0] += update
                 errors += int(update != 0)

ch02/test.py

@@ -28,22 +28,71 @@
 y = df.iloc[:, 4].values
 y = np.where(y == "Iris-setosa", -1, 1)
 
-X = df.iloc[:, [0, 2, 4]].values
-setosa_array = X[(X[:, 2] == "Iris-setosa")]
-virginica_array = X[(X[:, 2] == "Iris-virginica")]
-versicolor_array = X[(X[:, 2] == "Iris-versicolor")]
-
-plt.scatter(setosa_array[:, 0], setosa_array[:, 1],
-            color="red", marker="o", label="setosa")
-plt.scatter(virginica_array[:, 0], virginica_array[:, 1],
-            color="blue", marker="x", label="virginica")
-plt.scatter(versicolor_array[:, 0], versicolor_array[:, 1],
-            color="green", marker="^", label="versicolor")
+X = df.iloc[:100, [0, 2]].values
 
+# temp_X = df.iloc[:, [0, 2, 4]].values
+# setosa_array = temp_X[(temp_X[:, 2] == "Iris-setosa")]
+# virginica_array = temp_X[(temp_X[:, 2] == "Iris-virginica")]
+# versicolor_array = temp_X[(temp_X[:, 2] == "Iris-versicolor")]
+#
+# plt.scatter(setosa_array[:, 0], setosa_array[:, 1],
+#             color="red", marker="o", label="setosa")
+# plt.scatter(virginica_array[:, 0], virginica_array[:, 1],
+#             color="blue", marker="x", label="virginica")
+# plt.scatter(versicolor_array[:, 0], versicolor_array[:, 1],
+#             color="green", marker="^", label="versicolor")
+#
+# plt.xlabel("sepal length [cm]")
+# plt.ylabel("petal length [cm]")
+# plt.legend(loc="upper left")
+# plt.show()
+
+ppn = Perceptron(eta=0.1, n_iter=10)
+ppn.fit(X, y)
+# plt.plot(range(1, len(ppn.error_) + 1),
+#          ppn.error_, marker="o")
+# plt.xlabel("Epochs")
+# plt.xlabel("Number of updates")
+# plt.show()
+
+from matplotlib.colors import ListedColormap
+
+for idx, cl in enumerate(np.unique(y)):
+    print(f"index : {idx}, cl {cl}")
+    print(X[y == cl, 0])
+
+def plot_decision_regions(X, y, classifier: Perceptron, resolution=0.02):
+    markers = ("s", "x", "o", "^", "v")
+    colors = ("red", "blue", "lightgreen", "gray", "cyan")
+    cmap = ListedColormap(colors[:len(np.unique(y))])
+
+    x1_min = X[:, 0].min() - 1
+    x1_max = X[:, 0].max() + 1
+
+    x2_min = X[:, 1].min() - 1
+    x2_max = X[:, 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=0.3, 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=X[y == cl, 0],
+                    y=X[y == cl, 1],
+                    alpha=0.8,
+                    c=cmap[idx],
+                    marker=markers[idx],
+                    llabel=cl,
+                    edgecolors="black")
+
+
+plot_decision_regions(X, y, classifier=ppn)
 plt.xlabel("sepal length [cm]")
 plt.ylabel("petal length [cm]")
 plt.legend(loc="upper left")
-plt.show()
-
-ppn = Perceptron(eta=0.1, n_iter=10)
-# ppn.fit(X, y)
+plt.show()