问感知器学不到正确的东西

EN

import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import datasets

class Perceptron(object):

def __init__(self, eta=0.01, n_iter=10):
    self.eta = eta
    self.n_iter = n_iter

def fit(self, X, y):
    self.w_ = np.zeros(1+ X.shape[1])
    self.errors_ = []

    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 != 0.0)
        self.errors_.append(errors)
    print(self.w_)
    return self

def net_input(self, X):
    return np.dot(X, self.w_[1:]) + self.w_[0]

def predict(self, X):
    return np.where(self.net_input(X) >= 0.0, 1, -1)

def main():
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)

X_train_01_subset = X_train
y_train_01_subset = y_train


y_train_01_subset[(y_train_01_subset == 0)] = -1
y_train_01_subset[(y_train_01_subset == 1)] = -1
y_train_01_subset[(y_train_01_subset == 2)] = 0

ppn01 = Perceptron(eta=0.1, n_iter=10)
ppn01.fit(X_train_01_subset, y_train_01_subset)

X_train_02_subset = X_train
y_train_02_subset = y_train

y_train_02_subset[(y_train_02_subset == 1)] = 1
y_train_02_subset[(y_train_02_subset == 0)] = -1
y_train_02_subset[(y_train_02_subset == 2)] = -1

ppn02 = Perceptron(eta=0.1, n_iter=10)
ppn02.fit(X_train_02_subset, y_train_02_subset)


X_train_03_subset = X_train
y_train_03_subset = y_train

y_train_03_subset[(y_train_03_subset == 1)] = -1
y_train_03_subset[(y_train_03_subset == 0)] = 2
y_train_03_subset[(y_train_03_subset == 2)] = -1

ppn03 = Perceptron(eta=0.1, n_iter=10)  
ppn03.fit(X_train_03_subset, y_train_03_subset)