Question

我正在尝试遵循Daume的书

http://ciml.info/dl/v0_99/ciml-v0_99-ch04.pdf（第43页）。

使用numpy和不使用numpy来拟合python中的vanilla perceptron模型使用sciki-learn库。

该算法在书中给出

我们如何在实践中实施这个模型？

到目前为止，我已经学会了如何阅读数据和标签：

def read_data(infile):
    data = np.loadtxt(infile)
    X = data[:,:-1]
    Y = data[:,-1]

    return X, Y

帮助将不胜感激!!

Answer 1

我想出的一个方法是：

（总是欢迎更好的想法！）

#!python
# -*- coding: utf-8 -*-#
"""
Perceptron Algorithm.

@author: Bhishan Poudel

@date:  Oct 31, 2017

"""
# Imports
import numpy as np
import matplotlib.pyplot as plt
from numpy.linalg import norm
import os, shutil
np.random.seed(100)

def read_data(infile):
    data = np.loadtxt(infile)
    X = data[:,:-1]
    Y = data[:,-1]

    return X, Y

def plot_boundary(X,Y,w,epoch):
    try:
        plt.style.use('seaborn-darkgrid')
        # plt.style.use('ggplot')
        #plt.style.available
    except:
        pass

    # Get data for two classes
    idxN = np.where(np.array(Y)==-1)
    idxP = np.where(np.array(Y)==1)
    XN = X[idxN]
    XP = X[idxP]

    # plot two classes
    plt.scatter(XN[:,0],XN[:,1],c='b', marker='_', label="Negative class")
    plt.scatter(XP[:,0],XP[:,1],c='r', marker='+', label="Positive class")
    # plt.plot(XN[:,0],XN[:,1],'b_', markersize=8, label="Negative class")
    # plt.plot(XP[:,0],XP[:,1],'r+', markersize=8, label="Positive class")
    plt.title("Perceptron Algorithm iteration: {}".format(epoch))

    # plot decision boundary orthogonal to w
    # w is w2,w1, w0  last term is bias.
    if len(w) == 3:
        a  = -w[0] / w[1]
        b  = -w[0] / w[2]
        xx = [ 0, a]
        yy = [b, 0]
        plt.plot(xx,yy,'--g',label='Decision Boundary')

    if len(w) == 2:
        x2=[ w[0],  w[1],  -w[1],  w[0]]
        x3=[ w[0],  w[1],   w[1], -w[0]]

        x2x3 =np.array([x2,x3])
        XX,YY,U,V = list(zip(*x2x3))
        ax = plt.gca()
        ax.quiver(XX,YY,U,V,scale=1, color='g')

    # Add labels
    plt.xlabel('X')
    plt.ylabel('Y')

    # limits
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    plt.xlim(x_min,x_max)
    plt.ylim(y_min,y_max)

    # lines from origin
    plt.axhline(y=0, color='k', linestyle='--',alpha=0.2)
    plt.axvline(x=0, color='k', linestyle='--',alpha=0.2)
    plt.grid(True)
    plt.legend(loc=1)
    plt.show()

    # Always clost the plot
    plt.close()


def predict(X,w):
    return np.sign(np.dot(X, w))

def plot_contour(X,Y,w,mesh_stepsize):
    try:
        plt.style.use('seaborn-darkgrid')
        # plt.style.use('ggplot')
        #plt.style.available
    except:
        pass    
    # Get data for two classes
    idxN = np.where(np.array(Y)==-1)
    idxP = np.where(np.array(Y)==1)
    XN = X[idxN]
    XP = X[idxP]

    # plot two classes with + and - sign
    fig, ax = plt.subplots()
    ax.set_title('Perceptron Algorithm')
    plt.xlabel("X")
    plt.ylabel("Y")
    plt.plot(XN[:,0],XN[:,1],'b_', markersize=8, label="Negative class")
    plt.plot(XP[:,0],XP[:,1],'y+', markersize=8, label="Positive class")
    plt.legend()

    # create a mesh for contour plot
    # We first make a meshgrid (rectangle full of pts) from xmin to xmax and ymin to ymax.
    # We then predict the label for each grid point and color it.
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1

    # Get 2D array for grid axes xx and yy  (shape = 700, 1000)
    # xx has 700 rows.
    # xx[0] has 1000 values.
    xx, yy = np.meshgrid(np.arange(x_min, x_max, mesh_stepsize),
                         np.arange(y_min, y_max, mesh_stepsize))

    # Get 1d array for x and y axes
    xxr = xx.ravel()  # shape (700000,)
    yyr = yy.ravel()  # shape (700000,)

    # ones vector
    # ones = np.ones(xxr.shape[0]) # shape (700000,)
    ones = np.ones(len(xxr)) # shape (700000,)

    # Predict the score
    Xvals  = np.c_[ones, xxr, yyr]
    scores = predict(Xvals, w)

    # Plot contour plot
    scores = scores.reshape(xx.shape)
    ax.contourf(xx, yy, scores, cmap=plt.cm.Paired)
    # print("xx.shape = {}".format(xx.shape))               # (700, 1000)
    # print("scores.shape = {}".format(scores.shape))       # (700, 1000)
    # print("scores[0].shape = {}".format(scores[0].shape)) # (1000,)

    # show the plot
    plt.savefig("Perceptron.png")
    plt.show()
    plt.close()

def perceptron_sgd(X, Y,epochs):
    """
    X: data matrix without bias.
    Y: target
    """
    # add bias to X's first column
    ones = np.ones(X.shape[0]).reshape(X.shape[0],1)
    X1 = np.append(ones, X, axis=1)


    w = np.zeros(X1.shape[1])
    final_iter = epochs

    for epoch in range(epochs):
        print("\n")
        print("epoch: {} {}".format(epoch, '-'*30))

        misclassified = 0
        for i, x in enumerate(X1):
            y = Y[i]
            h = np.dot(x, w)*y

            if h <= 0:
                w = w + x*y
                misclassified += 1
                print('misclassified? yes  w: {} '.format(w,i))

            else:
                print('misclassified? no  w: {}'.format(w))
                pass

        if misclassified == 0:
            final_iter = epoch
            break

    return w, final_iter

def gen_lin_separable_data(data, data_tr, data_ts,data_size):
    mean1 = np.array([0, 2])
    mean2 = np.array([2, 0])
    cov = np.array([[0.8, 0.6], [0.6, 0.8]])
    X1 = np.random.multivariate_normal(mean1, cov, size=int(data_size/2))
    y1 = np.ones(len(X1))
    X2 = np.random.multivariate_normal(mean2, cov, size=int(data_size/2))
    y2 = np.ones(len(X2)) * -1


    with open(data,'w') as fo, \
         open(data_tr,'w') as fo1, \
         open(data_ts,'w') as fo2:
        for i in range( len(X1)):
            line = '{:5.2f} {:5.2f} {:5.0f} \n'.format(X1[i][0], X1[i][1], y1[i])
            line2 = '{:5.2f} {:5.2f} {:5.0f} \n'.format(X2[i][0], X2[i][1], y2[i])
            fo.write(line)
            fo.write(line2)

        for i in range( len(X1) - 20):
            line = '{:5.2f} {:5.2f} {:5.0f} \n'.format(X1[i][0], X1[i][1], y1[i])
            line2 = '{:5.2f} {:5.2f} {:5.0f} \n'.format(X2[i][0], X2[i][1], y2[i])
            fo1.write(line)
            fo1.write(line2)

        for i in range((len(X1) - 20), len(X1) ):
            line = '{:5.2f} {:5.2f} {:5.0f} \n'.format(X1[i][0], X1[i][1], y1[i])
            line2 = '{:5.2f} {:5.2f} {:5.0f} \n'.format(X2[i][0], X2[i][1], y2[i])
            fo2.write(line)
            fo2.write(line2)

def main():
    """Run main function."""

    # generate linearly separable data
    data = 'data.txt'
    data_tr = 'data_train.txt'
    data_ts = 'data_test.txt'
    data_size = 200
    gen_lin_separable_data(data, data_tr, data_ts,data_size)

    # read data
    epochs = 20
    X_train, Y_train = read_data(data_tr)
    X_test, Y_test = read_data(data_ts)

    # fit perceptron 
    w, final_iter = perceptron_sgd(X_train,Y_train,epochs)
    print('w = ', w)

    plot_boundary(X_test,Y_test,w,final_iter)

    # contour plot
    mesh_stepsize = 0.01
    plot_contour(X_test,Y_test,w,mesh_stepsize)

if __name__ == "__main__":
    main()

决策边界如下：

Answer 2

我最近的回购邮件中有一个感知器实现：NP_ML。示例结果是：

如何在Python中实现Perceptron？

2 个答案: