Библиотека собеса по Data Science | вопросы с собеседований

Напишите логистическую регрессию

import numpy as np

class LogisticRegression:
    
    def __init__(self, learning_rate=0.01, n_iters=1000):
        self.learning_rate = learning_rate
        self.n_iters = n_iters
        self.weights = None
        self.bias = None
        
    def fit(self, X, y):
        # initialize weights and bias to zeros
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0
        
        # gradient descent optimization
        for i in range(self.n_iters):
            # calculate predicted probabilities and cost
            z = np.dot(X, self.weights) + self.bias
            y_pred = self._sigmoid(z)
            cost = (-1 / n_samples) * np.sum(y * np.log(y_pred) + (1 - y) * np.log(1 - y_pred))
            
            # calculate gradients
            dw = (1 / n_samples) * np.dot(X.T, (y_pred - y))
            db = (1 / n_samples) * np.sum(y_pred - y)
            
            # update weights and bias
            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db
            
    def predict(self, X):
        # calculate predicted probabilities
        z = np.dot(X, self.weights) + self.bias
        y_pred = self._sigmoid(z)
        # convert probabilities to binary predictions
        return np.round(y_pred).astype(int)
    
    def _sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

#python
#машинное_обучение

www.tg-me.com/pl/Библиотека собеса по Data Science | вопросы с собеседований/com.ds_interview_lib/611

1.2K viewsSep 26, 2024 at 12:21

Напишите логистическую регрессию

import numpy as np

class LogisticRegression:
    
    def __init__(self, learning_rate=0.01, n_iters=1000):
        self.learning_rate = learning_rate
        self.n_iters = n_iters
        self.weights = None
        self.bias = None
        
    def fit(self, X, y):
        # initialize weights and bias to zeros
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0
        
        # gradient descent optimization
        for i in range(self.n_iters):
            # calculate predicted probabilities and cost
            z = np.dot(X, self.weights) + self.bias
            y_pred = self._sigmoid(z)
            cost = (-1 / n_samples) * np.sum(y * np.log(y_pred) + (1 - y) * np.log(1 - y_pred))
            
            # calculate gradients
            dw = (1 / n_samples) * np.dot(X.T, (y_pred - y))
            db = (1 / n_samples) * np.sum(y_pred - y)
            
            # update weights and bias
            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db
            
    def predict(self, X):
        # calculate predicted probabilities
        z = np.dot(X, self.weights) + self.bias
        y_pred = self._sigmoid(z)
        # convert probabilities to binary predictions
        return np.round(y_pred).astype(int)
    
    def _sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

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