【头歌】机器学习 --- 感知机

第1关：感知机 - 西瓜好坏自动识别

#encoding=utf8
import numpy as np
#构建感知机算法
class Perceptron(object):def __init__(self, learning_rate = 0.01, max_iter = 200):self.lr = learning_rateself.max_iter = max_iterdef fit(self, data, label):'''input:data(ndarray):训练数据特征label(ndarray):训练数据标签output:w(ndarray):训练好的权重b(ndarry):训练好的偏置'''#编写感知机训练方法，w为权重，b为偏置self.w = np.array([1.]*data.shape[1])self.b = np.array([1.])#********* Begin *********#i = 0while i < self.max_iter:flag = Truefor j in range(len(label)):if label[j] * (np.inner(self.w, data[j]) + self.b) <= 0:flag = Falseself.w += self.lr * (label[j] * data[j])self.b += self.lr * label[j]if flag: breaki+=1#********* End *********#def predict(self, data):'''input:data(ndarray):测试数据特征output:predict(ndarray):预测标签'''#********* Begin *********#y = np.inner(data, self.w) + self.bfor i in range(len(y)): # range(0,6) if y[i] >= 0:y[i] = 1else:y[i] = -1predict = y#********* End *********#return predict

第2关：scikit-learn感知机实践 - 癌细胞精准识别

#encoding=utf8
import osif os.path.exists('./step2/result.csv'):os.remove('./step2/result.csv')#********* Begin *********#
import pandas as pd
train_data = pd.read_csv('./step2/train_data.csv')
train_label = pd.read_csv('./step2/train_label.csv')
train_label = train_label['target']
test_data = pd.read_csv('./step2/test_data.csv')
from sklearn.linear_model.perceptron import Perceptron
clf = Perceptron(eta0 = 0.01,max_iter = 200)
clf.fit(train_data, train_label)
result = clf.predict(test_data)
frameResult = pd.DataFrame({'result':result})
frameResult.to_csv('./step2/result.csv', index = False)
#********* End *********#

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