اگر دوست دارید کد بزاریم و تحلیل کنیم در کنار بقیه محتوای کانال این پیام را لایک کنین.

from sklearn.datasets import make_classification
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np

# Generate a 2D classification dataset
X, y = make_classification(n_samples=100, n_features=2, n_redundant=0,
                           n_informative=2, random_state=42, n_clusters_per_class=1)

# Train a linear SVM
clf = SVC(kernel='linear')
clf.fit(X, y)

# Create mesh grid for decision boundary
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 500),
                     np.linspace(y_min, y_max, 500))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

# Plot the data and decision boundary
plt.figure(figsize=(8, 6))
plt.contourf(xx, yy, Z, alpha=0.3)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', s=50)

# Plot decision boundary and margins
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
w = clf.coef_[0]
b = clf.intercept_[0]
xx_plot = np.linspace(xlim[0], xlim[1])
yy_plot = -(w[0] * xx_plot + b) / w[1]
plt.plot(xx_plot, yy_plot, 'k-')

# Plot margins
margin = 1 / np.sqrt(np.sum(w ** 2))
yy_margin_up = yy_plot + np.sqrt(1 + w[0] ** 2 / w[1] ** 2) * margin
yy_margin_down = yy_plot - np.sqrt(1 + w[0] ** 2 / w[1] ** 2) * margin
plt.plot(xx_plot, yy_margin_up, 'k--')
plt.plot(xx_plot, yy_margin_down, 'k--')

# Plot support vectors
plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,
            facecolors='none', edgecolors='r', linewidths=1.5, label='Support Vectors')

plt.noscript("SVM Classification with Linear Kernel")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.legend()
plt.tight_layout()
plt.show()

از این به بعد کد های بیشتری قرار میدم برای هر بخش که راحت بتونید کپی کنین و استفاده کنید . اگر دوست دارید حتما ری اکشن بزنید. 💕

# Looping through a list
fruits = ["apple", "banana", "cherry"]
for fruit in fruits:
    print(fruit)  # Output: apple, banana, cherry

# Looping through a string
for char in "hello":
    print(char)  # Output: h, e, l, l, o

# Using range to repeat a loop 5 times
for i in range(5):
    print(i)  # Output: 0, 1, 2, 3, 4

# Looping with break
for num in range(10):
    if num == 5:
        break
    print(num)  # Output: 0 to 4

# Looping with continue
for i in range(5):
    if i == 2:
        continue
    print(i)  # Output: 0, 1, 3, 4

کد تصویر بالا

# Import necessary libraries
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt
import numpy as np

# Create a 2D classification dataset with 2 informative features
X, y = make_classification(
    n_samples=200,
    n_features=2,
    n_redundant=0,
    n_informative=2,
    n_clusters_per_class=1,
    random_state=42
)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

# Train a Gaussian Naive Bayes classifier
model = GaussianNB()
model.fit(X_train, y_train)

# Create a mesh grid for visualization of decision boundary
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(
    np.linspace(x_min, x_max, 500),
    np.linspace(y_min, y_max, 500)
)

# Predict class labels for each point in the mesh grid
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

# Plot the decision boundary and data points
plt.figure(figsize=(8, 6))
plt.contourf(xx, yy, Z, alpha=0.3)  # Background classification regions
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k')  # Original data points
plt.noscript("Naive Bayes Classification (GaussianNB)")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.tight_layout()
plt.show()

در ادامه کد تصویر بالا رو قرار داده ام می توانید کپی کنید تغییر بدید و استفاده کنید.

# Import required libraries
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
import matplotlib.pyplot as plt
import numpy as np

# Create a non-linear 2D dataset shaped like moons
X, y = make_moons(n_samples=300, noise=0.2, random_state=42)

# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

# Initialize a neural network model with 2 hidden layers (10 and 5 neurons)
mlp = MLPClassifier(hidden_layer_sizes=(10, 5), max_iter=2000, random_state=42)

# Train the model using the training data
mlp.fit(X_train, y_train)

# Create a mesh grid to visualize decision boundaries
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
xx, yy = np.meshgrid(
    np.linspace(x_min, x_max, 500),
    np.linspace(y_min, y_max, 500)
)

# Predict the class for each point in the mesh grid
Z = mlp.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

# Plot the decision boundary and original data points
plt.figure(figsize=(8, 6))
plt.contourf(xx, yy, Z, alpha=0.3)  # decision regions
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', s=50)  # input points
plt.noscript("Neural Network Classification (MLPClassifier)")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.tight_layout()
plt.show()