在matlab中做Regularized logistic regression 原理: 我的代码: function [J, grad] = costFunctionReg(theta, X, y,lambda)%COSTFUNCTIONREG Compute costandgradientforlogistic regression with regularization% J = COSTFUNCTIONREG(theta, X, y,lambda) computes the cost of using% theta as the parameterforr...
for the purpose of advanced robotic assisted Manufacturing. In this work, we have developed optimization code using logistic regression. This code can be very useful for manufacturing processes for separating manufactured goods into acceptable and non-acceptable classes. Machine Learning (ML) is a ...
Great! Our data is ready for our model! Building a Logistic Regression model Let’s start by splitting our data into a training set and test set (there is another test.csv file that you can play around with in case you want to use all this data for training)....
"prompt": "X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1) #Train a logistic regression model, predict the labels on the test set and compute the accuracy score", "temperature": 0.1, "top_p": 0.7, "max_tokens": 512, "seed": 42, "stream": False ...
(X, y, test_size=0.1) #Train a logistic regression model, predict the labels on the test set and compute the accuracy score", "temperature": 0.1, "top_p": 0.7, "max_tokens": 512, "seed": 42, "stream": False } # re-use connections session = requests.Session() response = ...
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"prompt": "X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1) #Train a logistic regression model, predict the labels on the test set and compute the accuracy score", "temperature": 0.1, "top_p": 0.7, ...
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...
d Heading angle (top) and lateral position (bottom) of a mouse in VR for four trial types shown as dashed lines and colored as in panel (b). e Logistic regression to explain the mouse’s choice based on the sample cue identity vs. running patterns in the delay segment. Beta ...
% 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, ...