[Course] Page 025 had write

Type : feat.

Abstract:
* Page 025 had write

ch02/perceptron.py

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+###
+# page 025
+##
+import numpy as np
+
+
+class Perceptron:
+    def __init__(self, eta=0.01, n_iter=50, random_state=1):
+        self.eta = eta
+        self.n_iter = n_iter
+        self.random_state = random_state
+
+    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.error_ = []
+
+        for _ in range(self.n_iter):
+            errors = 0
+            for xi, target in zip(X, y):
+                update = self.eta * (target - self.predict(xi))
+                self.w_[1:] += update * xi
+                self.w_[0] += update
+                errors += int(update != 0)
+            self.error_.append(errors)
+        return self
+
+    def net_input(self, X):
+        res = np.dot(X, self.w_[1:]) + self.w_[0]
+        return res
+
+    def predict(self, X):
+        res = np.Where(self.net_input(X) >= 0.0, 1, -1)
+        return res