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+# Лаб 1
+Работа с типовыми наборами данных и различными моделями
+# Вариант 3
+Данные: make_classification (n_samples=500, n_features=2,
+n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1)
+# Модели:
+1. Линейную регрессию
+1. Полиномиальную регрессию (со степенью 3)
+1. Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0)
+# Screenshots
+
+<p>
+  <img src="screens/Screenshot_2022-07-20-15-27-01.png" width="200" title="пример 1">
+</p>
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diff --git a/antonov_dmitry_lab_1/lab1.py b/antonov_dmitry_lab_1/lab1.py
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+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
+
+X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, random_state=0, n_clusters_per_class=1)
+rng = np.random.RandomState(2)
+X += 2 * rng.uniform(size=X.shape)
+linearly_dataset = (X, y)
+moon_dataset = make_moons(noise=0.3, random_state=0)
+circles_dataset = make_circles(noise=0.2, factor=0.5, random_state=1)
+
+datasets = [moon_dataset, circles_dataset, linearly_dataset]
+for ds_cnt, ds in enumerate(datasets):
+    X, y = ds
+    X = StandardScaler().fit_transform(X)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42)
+
+    alphas = np.logspace(-5, 3, 5)
+
+    current_subplot = plt.subplot(3, 5 + 1, i)
+
+current_subplot.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
+current_subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
+
+cm = plt.cm.RdBu
+cm_bright = ListedColormap(['#FF0000', '#0000FF'])
+
+h = .02  # шаг регулярной сетки
+x0_min, x0_max = X[:, 0].min() - .5, X[:, 0].max() + .5
+x1_min, x1_max = X[:, 1].min() - .5, X[:, 1].max() + .5
+xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h), np.arange(x1_min, x1_max, h))
+
+Z = clf.decision_function(np.c_[xx0.ravel(), xx1.ravel()])  # 1
+Z = clf.predict_proba(np.c_[xx0.ravel(), xx1.ravel()])[:, 1]  # 2
+Z = clf.predict(np.c_[xx0.ravel(), xx1.ravel()])  # 3
+
+hasattr(clf, "decision_function")
+
+Z = Z.reshape(xx0.shape)
+current_subplot.contourf(xx0, xx1, Z, cmap=cm, alpha=.8)
+current_subplot.set_xlim(xx0.min(), xx0.max())
+current_subplot.set_ylim(xx0.min(), xx1.max())
+current_subplot.set_xticks(())
+current_subplot.set_yticks(())
+current_subplot.set_title(name)
+current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score).lstrip('0'),
+                     size=15, horizontalalignment='right')
+
+current_subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
+
+figure.subplots_adjust(left=.02, right=.98)
+
+current_subplot.set_title("Input data")
+current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score).lstrip('0'), size=15,
+                     horizontalalignment='right')