Python——scikit-learn實現單層感知器

scikit-learn 提供了感知器功能。和我們用過的其他功能類似，Perceptron類的構造器接受超引數設定。Perceptron類有fit_transform()和predict()方法。Perceptron類還提供了partial_fit()方法，允許分類器訓練流式資料（streaming data）並做出預測。

# coding=utf-8
from sklearn.datasets import fetch_20newsgroups
from sklearn.metrics import f1_score, classification_report
from
 
 sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import Perceptron
categories = ['rec.sport.hockey', 'rec.sport.baseball', 'rec.autos']
newsgroups_train = fetch_20newsgroups(subset='train', categories=categories, remove=('headers', 'footers', 'quotes'))
newsgroups_test = fetch_20newsgroups(subset='test'
 
, categories=categories, remove=('headers', 'footers', 'quotes'))

vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(newsgroups_train.data)
X_test = vectorizer.trasform(X_train, newsgroups_train.target)

classifier = Perceptron(n_iter=100, eta0=0.1)
classifier.fit_transform(X_train, newsgroups_train.target)
predictions = classifier.predict(X_test)
print
 
 classification_report(newsgroups_test.target, predictions)