DS_INTERVIEW_LIB Telegram 611
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
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tgoop.com/ds_interview_lib/611
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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

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