الگوریتم های یادگیری ماشین رایج با SciPy

from scipy.optimize import minimize
from scipy.stats import linregress

# Define the data
X = [1, 2, 3, 4, 5]
y = [2, 4, 5, 4, 5]

# Define the loss function
def squared_error(params):
    w, b = params
    return np.sum((y - (w * X + b)) ** 2)

# Minimize the loss function
initial_guess = [0, 0]  # Initialize weights and bias
result = minimize(squared_error, initial_guess)

# Extract the fitted parameters
w, b = result.x
print("Fitted parameters:", w, b)

# Make predictions
new_X = [6, 7]
predictions = w * new_X + b
print("Predictions:", predictions)

from scipy.spatial.distance import cdist
from scipy.stats import mode

# Define the training data
X_train = [[1, 2], [3, 4], [5, 6], [7, 8]]
y_train = [0, 1, 1, 0]

# Define the new data point to classify
new_X = [9, 10]

# Compute distances to all training points
distances = cdist([new_X], X_train, metric='euclidean')

# Find the k nearest neighbors
k = 3  # Number of nearest neighbors
nearest_neighbors = np.argmin(distances, axis=1)[:k]

# Get the class labels of the nearest neighbors
nearest_neighbors_labels = y_train[nearest_neighbors]

# Predict the class of the new data point
predicted_class = mode(nearest_neighbors_labels)[0]
print("Predicted class:", predicted_class)

from scipy.cluster.kmeans import kmeans

# Define the data
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14]]

# Perform k-means clustering with k=3 clusters
kmeans_result = kmeans(X, k=3)

# Get the cluster labels for each data point
cluster_labels = kmeans_result.labels_

print("Cluster labels:", cluster_labels)