51 lines
2.5 KiB
Python
51 lines
2.5 KiB
Python
import random
|
||
import numpy as np
|
||
from matplotlib import pyplot as plt
|
||
from matplotlib.colors import ListedColormap
|
||
from sklearn.model_selection import train_test_split
|
||
from sklearn.preprocessing import StandardScaler
|
||
from sklearn.datasets import make_moons, make_circles, make_classification
|
||
from sklearn.neural_network import MLPClassifier
|
||
from sklearn.linear_model import LinearRegression
|
||
from sklearn.preprocessing import PolynomialFeatures
|
||
from sklearn.linear_model import Perceptron
|
||
|
||
rs = random.randrange(100)
|
||
X, y = make_moons(n_samples=200, noise=0.3, random_state=rs)
|
||
X_train, X_Test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
|
||
print("Линейная регрессия")
|
||
linerModel = LinearRegression().fit(X_train, y_train)
|
||
print("результат модели на учебных данных =", linerModel.score(X_train, y_train))
|
||
print("результат модели на тестовых данных =", linerModel.score(X_Test, y_test))
|
||
print("Многослойный персептрон с 10-ю нейронами в скрытом слое")
|
||
mlp = MLPClassifier(hidden_layer_sizes=(10), alpha = 0.01, max_iter=2000).fit(X_train, y_train)
|
||
print("результат модели на учебных данных =", mlp.score(X_train, y_train))
|
||
print("результат модели на тестовых данных =", mlp.score(X_Test, y_test))
|
||
print("Персептрон ")
|
||
perceptron = Perceptron().fit(X_train, y_train)
|
||
print("результат модели на учебных данных =", perceptron.score(X_train, y_train))
|
||
print("результат модели на тестовых данных =", perceptron.score(X_Test, y_test))
|
||
|
||
plt.xlabel("Свойство 1")
|
||
plt.ylabel("Свойство 2")
|
||
plt.title("Сгенерированные данные")
|
||
plt.scatter(X[:, 0], X[:, 1], c = y, cmap = plt.cm.Set1)
|
||
plt.show()
|
||
|
||
h = 0.01
|
||
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
|
||
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
|
||
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
|
||
|
||
def showPlot(name, model):
|
||
plt.title(name)
|
||
c = model.predict(np.c_[xx.ravel(), yy.ravel()])
|
||
c = np.where(c >= 0.5, 1, 0)
|
||
c = c.reshape(xx.shape)
|
||
plt.contourf(xx, yy, c, cmap = plt.cm.Set1, alpha=0.2)
|
||
plt.scatter(X[:, 0], X[:, 1], c = y, cmap = plt.cm.Set1)
|
||
plt.show()
|
||
|
||
showPlot("Линейная регрессия", linerModel)
|
||
showPlot("Многослойный персептрон", mlp)
|
||
showPlot("Персептрон", perceptron) |