x = StandardScaler().fit_transform(x) lr = LogisticRegression() # Logistic回归模型 lr.fit(x, y.ravel()) # 根据数据[x,y],计算回归参数 # 等价形式 lr = Pipeline([(‘sc’, StandardScaler()), (‘clf’, LogisticRegression()) ]) lr.fit(x, y.ravel()) # 画图 N, M = 500, 500 #...
Google. 这个适合于搜索单纯的某个已经比较成熟的算法而不是整个论文的全貌,比如直接搜索 KNN in Python, Logistic Regression in R 之类的。一般都能搜到算法详解和详细code, 改吧改吧就能跑自己的了。 Github. github中搜索论文名称, 或对应的算法名称,最好是算法关键词。这样一般可以搜到一堆相关算法,个个都...
When programming in Python, indentation is something that you will definitely use. However, you should be careful with it, as it can lead to syntax errors. The recommendation is, therefore, to use four spaces for indentation. For example, this statement uses four spaces of indentation: if Tru...
Introduction to medical image analysis - An introduction to how to use machine learning for image medical analysis. Articles and tutorials in Python. Graph machine learning - A series dedicated to graphs: what they are, how you can work with them, and which algorithms and tasks you can do. ...
Tokenization, Stemming, and Lemmatization in Python. (code) How to Fine Tune BERT for Semantic Textual Similarity using Transformers in Python. (code) How to Calculate the BLEU Score in Python. (code) Word Error Rate in Python. (code) How to Calculate ROUGE Score in Python. (code) Visual...
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LogisticRegression LeNetConvPoolLayer 这些类大多数的实现都在init方法中: 1、首先接收这一层的输入输出的尺寸和这一层的输入数据。 2、然后初始化这层的参数,参数都是theano.shared。 3、对于给定的输入和参数,构建这层的输出。 在conv_net_sentences.py中 获取训练数据和测试数据以后,绝大部分的工作由train_co...
% grad = (unregularized gradient for logistic regression) % temp = theta; % temp(1) = 0; % because we don't add anything for j = 0 % grad = grad + YOUR_CODE_HERE (using the temp variable) % h=sigmoid(X*theta); for i=1:m, ...
Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requires cuML >= 0.15 🖥️ PyCaret Intel sklearnex support You can apply Intel optimizations for machine learning algorithms...
random.permutation(len(mist['data'])) X, y = mnist['data'][shuffle_index], mnist['target'][shuffle_index] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42) lr = LogisticRegression() lr.fit(X_train, y_train) y_hat = lr....