(obsolete) (ML) Example1

perceptron_example.py

# -*- coding: utf-8 -*-

#from ch02_1 import Perceptron
from perceptron import Perceptron, Perceptron2

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


if 0:
    #url = ('https://archive.ics.uci.edu/ml/'
    #       'machine-learning-databases/iris/iris.data')
    url = 'iris.data'
    df = pd.read_csv(url, header=None)
    #df.tail()

    # select setosa and versicolor
    y = df.iloc[0:100, 4].values
    y = np.where(y == 'Iris-setosa', -1, 1)
    #print(type(y))   #=> <class 'numpy.ndarray'>
    #print(y)         #=> [-1 -1 -1 ..... 1  1  1]
    y = [-1] * 50 + [1] * 50

    # extract sepal length and petal length
    X = df.iloc[0:100, [0, 2]].values
    print(type(X))    #=> <class 'numpy.ndarray'>
    print(X)

if 1:
    import sys
    ## load csv file
    import csv
    filename = 'iris.data'
    table__ = []
    with open(filename) as f:
        reader = csv.reader(f)
        for line in reader:
            v1, v2, v3, v4, v5 = line
            #      [sepal_h,   sepal_w,   pepal_h,   pepal_w,   label]
            row_ = [float(v1), float(v2), float(v3), float(v4), v5]
            table__.append(row_)

if 1:
    #     [sepal_h, pepal_h]
    input__ = [ [row_[0], row_[2]] for row_ in table__[0:100] ]
    #
    def fn(name):
        if   name == 'Iris-setosa':      return -1
        elif name == 'Iris-versicolor':  return 1
        else:
            raise ValueError("%r: unexpected value" % name)
    expected_ = [ fn(row_[4]) for row_ in table__[0:100] ]

#ppn = Perceptron(eta=0.1, n_iter=10)
ppn = Perceptron2(eta=0.1, n_iter=10)

ppn.fit(input__, expected_)

if 1:
    plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')
    plt.xlabel('Epochs')
    plt.ylabel('Number of misclassifications')

    plt.tight_layout()
    # plt.savefig('./perceptron_1.png', dpi=300)
    plt.show()