[Course] Draw data plot

Type : feat.

Abstract:
* Draw data plot

ch02/perceptron.py

@@ -6,19 +6,24 @@
 
 class Perceptron:
     def __init__(self, eta=0.01, n_iter=50, random_state=1):
-        self.eta = eta
+        self.error_ = None
+        self.w_ = None
+        self.eta = max(0.0, min(eta, 1.0))
         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.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):
+            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)
@@ -30,5 +35,5 @@ def net_input(self, X):
         return res
 
     def predict(self, X):
-        res = np.Where(self.net_input(X) >= 0.0, 1, -1)
-        return res
+        res = np.where(self.net_input(X) >= 0.0, 1, -1)
+        return res

ch02/test.py

@@ -0,0 +1,49 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+import requests
+import io
+import os
+from perceptron import Perceptron
+PATH = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+
+with requests.get(PATH) as response:
+    raw_data = response.text
+
+# s = os.path.join("https://archive.ics.uci.edu", "ml",
+#                  "machine-learning-databases",
+#                  "iris", "iris.data")
+s = os.path.join(PATH)
+
+df = pd.read_csv(s)
+# print(df.tail())
+# string_data = io.StringIO(raw_data)
+#
+# df = pd.read_csv(string_data, header=None, encoding="utf-8")
+#
+# print(df.tail())
+
+import matplotlib.pyplot as plot
+import numpy as np
+
+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")
+
+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)