доделка

alexandrov_dmitrii_lab_1/lab1.py

@@ -7,92 +7,50 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.pipeline import Pipeline
 
+rs = random.randrange(50)
+
+X, y = make_moons(n_samples=250, 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)
+
+figure = plt.figure(1, figsize=(16, 9))
+axis = figure.subplots(4, 3)
+cm = ListedColormap(['#FF0000', "#0000FF"])
+arr_res = list(range(len(y_test)))
+X_scale = list(range(len(y_test)))
+
+
+def test(col, model):
+    global axis
+    global arr_res
+    global X_test
+    global X_train
+    global y_train
+    global y_test
+
+    model.fit(X_train, y_train)
+    res_y = model.predict(X_test)
+    print(model.score(X_test, y_test))
+
+    axis[0, col].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm)
+    axis[1, col].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
+    axis[2, col].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
+    axis[2, col].scatter(X_test[:, 0], X_test[:, 1], c=res_y, cmap=cm)
+    axis[3, col].plot([i for i in range(len(res_y))], y_test, c="g")
+    axis[3, col].plot([i for i in range(len(res_y))], res_y, c="r")
+
 
 def start():
-    rs = random.randrange(10)
-    rs = 5
-
-    X, y = make_moons(n_samples=250, 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)
-
     lin = LinearRegression()
     poly = Pipeline([('poly', PolynomialFeatures(degree=3)),
                      ('linear', LinearRegression())])
     ridge = Pipeline([('poly', PolynomialFeatures(degree=3)),
-                     ('ridge', Ridge(alpha=1.0))])
+                      ('ridge', Ridge(alpha=1.0))])
 
-
+    test(0, lin)
-    figure = plt.figure(1, figsize=(16, 9))
+    test(1, poly)
-    axis = figure.subplots(4, 3)
+    test(2, ridge)
-    cm = ListedColormap(['#FF0000', "#0000FF"])
-    arr_res = list(range(len(y_test)))
-    X_scale = list(range(len(y_test)))
-
-
-    lin.fit(X_train, y_train)
-    res_y = lin.predict(X_test)
-    print(lin.score(X_test, y_test))
-
-    axis[0, 0].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm)
-    axis[1, 0].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
-    axis[3, 0].plot([i for i in range(len(res_y))], y_test, c="g")
-    axis[3, 0].plot([i for i in range(len(res_y))], res_y, c="r")
-
-    for i in range(len(X_test)):
-        arr_res[i] = [X_test[i], res_y[i], y_test[i]]
-    arr_res = sorted(arr_res, key=lambda x: x[1])
-    for i in range(len(X_test)):
-        X_scale[i] = arr_res[i][0]
-        res_y[i] = arr_res[i][1]
-        arr_res[i] = arr_res[i][2]
-
-    axis[2, 0].plot(X_scale, arr_res, c="g")
-    axis[2, 0].plot(X_scale, res_y, c="r")
-
-
-    poly.fit(X_train, y_train)
-    res_y = poly.predict(X_test)
-    print(poly.score(X_test, y_test))
-
-    axis[0, 1].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm)
-    axis[1, 1].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
-    axis[3, 1].plot([i for i in range(len(res_y))], y_test, c="g")
-    axis[3, 1].plot([i for i in range(len(res_y))], res_y, c="r")
-
-    for i in range(len(X_test)):
-        arr_res[i] = [X_test[i], res_y[i], y_test[i]]
-    arr_res = sorted(arr_res, key=lambda x: x[1])
-    for i in range(len(X_test)):
-        X_scale[i] = arr_res[i][0]
-        res_y[i] = arr_res[i][1]
-        arr_res[i] = arr_res[i][2]
-
-    axis[2, 1].plot(X_scale, arr_res, c="g")
-    axis[2, 1].plot(X_scale, res_y, c="r")
-
-
-    ridge.fit(X_train, y_train)
-    res_y = ridge.predict(X_test)
-    print(ridge.score(X_test, y_test))
-
-    axis[0, 2].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm)
-    axis[1, 2].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
-    axis[3, 2].plot([i for i in range(len(res_y))], y_test, c="g")
-    axis[3, 2].plot([i for i in range(len(res_y))], res_y, c="r")
-
-    for i in range(len(X_test)):
-        arr_res[i] = [X_test[i], res_y[i], y_test[i]]
-    arr_res = sorted(arr_res, key=lambda x: x[1])
-    for i in range(len(X_test)):
-        X_scale[i] = arr_res[i][0]
-        res_y[i] = arr_res[i][1]
-        arr_res[i] = arr_res[i][2]
-
-    axis[2, 2].plot(X_scale, arr_res, c="g")
-    axis[2, 2].plot(X_scale, res_y, c="r")
 
     plt.show()
 
 
 start()
-

alexandrov_dmitrii_lab_1/readme.md

@@ -11,7 +11,7 @@
 Файл lab1.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует, 
 
 ###Описание программы
-Генерирует один из 10 наборов данных, показывает окно с графиками и пишет оценку моделей обучения по заданию. 
+Генерирует один из 50 наборов данных, показывает окно с графиками и пишет оценку моделей обучения по заданию. 
 Использует библиотеки matplotlib для демонстрации графиков и sklearn для создания и использования моделей. 
 
 ###Результаты тестирования