csv

add csv

Data_learn.csv

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+Продукт,Сладость,Хруст,КлассЯблоко,7,7,0Салат,2,5,1Бекон,1,2,2Орехи,1,5,2Рыба,1,1,2Сыр,1,1,2Бананы,9,1,0Морковь,2,8,1Виноград,8,1,0Апельсин,6,1,0Мёд,30,0,3Хворост,28,2,3Зефир,29,1,3Мармелад,26,0,3Слива,7,0,0Сельдерей,0,6,1Шницель,0,1,2Мандарин,7,1,0Капуста,0,7,1Сухарики,2,8,2Ежевика,8,2,0Огурец,1,9,1Гренки,3,9,2Манго,8,3,0Фруктовая палочка,30,2,3Маршмеллоу,29,1,3

Laba_4.py

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+import csv
+import pandas as pds
+import numpy as nmp
+import pylab as graph
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import StandardScaler
+
+
+def graph_def(k, er, swt, crс, dta, clrs, cls):
+    graph.subplot(3, 1, 1)
+    graph.plot([i for i in range(1, k + 1)], er)
+    graph.title("Error plot for various k")
+    graph.xlabel("k")
+    graph.ylabel("Error")
+
+    colour_list = [clrs[str(i)] for i in cls]
+    graph.subplot(3, 1, 2)
+    graph.scatter(swt, crс, c=colour_list)
+    graph.title("Input Data chart")
+    graph.xlabel("Сладость")
+    graph.ylabel("Хруст")
+
+    colour_list = [clrs[str(i)] for i in dta]
+
+    graph.subplot(3, 1, 3)
+    graph.scatter(swt, crс, c=colour_list)
+    graph.title("Input Data chart")
+    graph.xlabel("Сладость")
+    graph.ylabel("Хруст")
+    # pylab.show()
+
+
+def method_knn(lData, tData, k, w_size, class_num):
+    Datas = []
+    for i in range(len(lData)):
+        Datas.append(lData[i])
+    for j in range(len(tData)):
+        Datas.append(tData[j])
+
+    l_size = len(lData) - 1
+    t_size = len(Datas) - 1 - l_size
+
+    k_max = k
+    final_dist = nmp.zeros((t_size, l_size))
+
+    for i in range(t_size):
+        for j in range(l_size):
+            final_dist[i][j] = (
+                                       (int(Datas[l_size + 1 + i][1]) - int(Datas[j + 1][1])) ** 2
+                                       + (int(Datas[l_size + 1 + i][2]) - int(Datas[j + 1][2])) ** 2
+                               ) ** (1 / 2)
+
+    er_k = [0] * k_max
+    for k in range(k_max):
+        print("Classification for k:", k + 1)
+        sucsess = 0
+        er = [0] * t_size
+        class_s = [0] * t_size
+
+        for i in range(t_size):
+            qwant_dist = [0] * class_num
+            print(str(i) + ". " + "We classify the product ", Datas[l_size + i + 1][0])
+            tmp = nmp.array(final_dist[i, :])
+            dist_max = max(tmp)
+
+            for j in range(k + 1):
+                ind_min = list(tmp).index(min(tmp))
+                if tmp[j] < w_size:
+                    qwant_dist[int(Datas[ind_min + 1][3])] += dist_max - tmp[j]
+                else:
+                    qwant_dist[int(Datas[ind_min + 1][3])] += 0
+
+                tmp[ind_min] = 1000
+                max1 = max(qwant_dist)
+
+                print("neighbor index = " + str(ind_min) + ", neighbor - " + Datas[ind_min + 1][0])
+                print("расстояние " + str(qwant_dist))
+            class_ind = list(qwant_dist).index(max1)
+            class_s[i] = class_ind
+
+            print("Assigning a class:" + Datas[l_size + i + 1][3])
+            print(class_s[i])
+            print(Datas[l_size + i + 1][3])
+            if int(class_s[i]) == int(Datas[l_size + i + 1][3]):
+                print("True")
+                sucsess += 1
+                er[i] = 0
+            else:
+                print("False")
+                er[i] = 1
+
+        er_k[k] = nmp.mean(er)
+
+        print("Error for " + str(k) + " neighbor")
+        print(er_k)
+
+    return er_k, class_s
+
+
+def method_sklearn(dat, clas, k, tsz):
+    X_train, X_test, y_train, y_test = train_test_split(dat, clas, test_size=tsz, random_state=0)
+    scaler = StandardScaler()
+    scaler.fit(X_train)
+    X_train = scaler.transform(X_train)
+    X_test = scaler.transform(X_test)
+    model = KNeighborsClassifier(n_neighbors=k)
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    print("Training sample parameters")
+    print(X_train)
+    print("Test sample options")
+    print(X_test)
+    print("Training sample class_s")
+    print(y_train)
+    print("Training sample class_s")
+    print(y_test)
+    print("Result")
+    print(predictions)
+    return X_train, X_test, y_train, y_test, predictions
+
+
+# number 1
+print("===============================================================")
+print("Solver #1")
+print("Input Data for products")
+Data = [["Продукт", "Сладость", "Хруст", "Класс"], ["Яблоко", "7", "7", "0"], ["Салат", "2", "5", "1"],
+        ["Бекон", "1", "2", "2"], ["Орехи", "1", "5", "2"], ["Рыба", "1", "1", "2"], ["Сыр", "1", "1", "2"],
+        ["Бананы", "9", "1", "0"], ["Морковь", "2", "8", "1"], ["Виноград", "8", "1", "0"], ["Апельсин", "6", "1", "0"],
+        ["Слива", "7", "0", "0"], ["Сельдерей", "0", "6", "1"], ["Шницель", "0", "1", "2"], ["Мандарин", "7", "1", "0"],
+        ["Капуста", "0", "7", "1"], ["Сухарики", "2", "8", "2"], ["Ежевика", "8", "2", "0"], ["Огурец", "1", "9", "1"],
+        ["Гренки", "3", "9", "2"], ["Манго", "8", "3", "0"], ]
+with open("Data_learn.csv", "w", encoding="utf8") as f:
+    writer = csv.writer(f, lineterminator="\r")
+    for row in Data:
+        writer.writerow(row)
+print("Incoming Data")
+print(Data)
+print("===============================================================")
+# number2
+print("===============================================================")
+print("Solver #2")
+print("Obtaining a classifier using the KNN method")
+k_max = 5
+window = 7
+er_k, class_s = method_knn(Data[0:11], Data[11:], k_max, window, 3)
+Dataset = pds.read_csv("Data_learn.csv")
+start_Data = Dataset[:10]["Класс"]
+s1 = pds.Series(class_s)
+start_Data = pds.concat([start_Data, s1])
+sweet = Dataset["Сладость"]
+crunch = Dataset["Хруст"]
+colours = {"0": "orange", "1": "blue", "2": "green"}
+class_s_info = Dataset["Класс"]
+print(start_Data)
+print(sweet)
+
+graph.figure()
+graph_def(k_max, er_k, sweet, crunch, start_Data, colours, class_s_info)
+k_max = 2
+my_Dataset = pds.read_csv("Data_learn.csv")
+sweetness = my_Dataset["Сладость"]
+crunch = my_Dataset["Хруст"]
+values = nmp.array(list(zip(sweetness, crunch)), dtype=nmp.float64)
+class_s = my_Dataset["Класс"]
+test_size = 0.5
+X_train, X_test, y_train, y_test, predictions = method_sklearn(values, class_s, k_max, test_size)
+colours = {"0": "orange", "1": "blue", "2": "green"}
+class_s_info = my_Dataset["Класс"]
+start_Data = my_Dataset[:10]["Класс"]
+s1 = nmp.concatenate((y_train, y_test), axis=0)
+s1 = pds.Series(s1)
+predictions = pds.Series(predictions)
+start_Data = pds.Series(start_Data)
+start_Data = pds.concat([start_Data, predictions])
+
+er = 0
+ct = 0
+
+truthclass_s = pds.Series(my_Dataset["Класс"])
+testclass_s = pds.concat([pds.Series(my_Dataset[:10]["Класс"]), predictions])
+
+print("Error count:")
+for i in testclass_s:
+    print(str(i) + " " + str(truthclass_s[ct]))
+    if i == truthclass_s[ct]:
+        er += 0
+    else:
+        er += 1
+ct += 1
+er = er / ct
+print(er)
+er_k = []
+for i in range(1, k_max + 1):
+    er_k.append(er)
+graph_def(k_max, er_k, sweet, crunch, start_Data, colours, class_s_info)
+print("===============================================================")
+
+print("===============================================================")
+print("Solver 3")
+print("New Data for learn")
+new_Data = Data[0:11]
+new_Data.append(["Мёд", "30", "0", "3"])
+new_Data.append(["Хворост", "28", "2", "3"])
+new_Data.append(["Зефир", "29", "1", "3"])
+new_Data.append(["Мармелад", "26", "0", "3"])
+
+new_Data = new_Data + Data[11:]
+new_Data.append(["Фруктовая палочка", "30", "2", "3"])
+new_Data.append(["Маршмеллоу", "29", "1", "3"])
+
+print("New Data")
+print(new_Data)
+
+with open("Data_learn.csv", "w", encoding="utf8") as f:
+    writer = csv.writer(f, lineterminator="\r")
+    for row in new_Data:
+        writer.writerow(row)
+
+print("\nKnn method for new Data")
+
+k_max = 5
+window = 7
+er_k, class_s = method_knn(new_Data[0:15], new_Data[15:], k_max, window, 4)
+Dataset = pds.read_csv("Data_learn.csv")
+start_Data = Dataset[:14]["Класс"]
+s1 = pds.Series(class_s)
+start_Data = pds.concat([start_Data, s1])
+
+sweet = Dataset["Сладость"]
+crunch = Dataset["Хруст"]
+colours = {"0": "orange", "1": "blue", "2": "green", "3": "red"}
+class_s_info = Dataset["Класс"]
+graph_def(k_max, er_k, sweet, crunch, start_Data, colours, class_s_info)
+
+print("\nSk Knn for new Data")
+
+k_max = 2
+
+my_Dataset = pds.read_csv("Data_learn.csv")
+sweetness = my_Dataset["Сладость"]
+crunch = my_Dataset["Хруст"]
+
+values = nmp.array(list(zip(sweetness, crunch)), dtype=nmp.float64)
+class_s = my_Dataset["Класс"]
+test_size = 0.461
+X_train, X_test, y_train, y_test, predictions = method_sklearn(values, class_s, k_max, test_size)
+colours = {"0": "orange", "1": "blue", "2": "green", "3": "red"}
+class_s_info = my_Dataset["Класс"]
+start_Data = my_Dataset[:14]["Класс"]
+s1 = nmp.concatenate((y_train, y_test), axis=0)
+s1 = pds.Series(s1)
+predictions = pds.Series(predictions)
+start_Data = pds.Series(start_Data)
+start_Data = pds.concat([start_Data, predictions])
+
+er = 0
+ct = 0
+
+truthclass_s = pds.Series(my_Dataset["Класс"])
+testclass_s = pds.concat([pds.Series(my_Dataset[:14]["Класс"]), predictions])
+
+print("Error count")
+for i in testclass_s:
+    print(str(i) + " " + str(truthclass_s[ct]))
+    if i == truthclass_s[ct]:
+        er += 0
+    else:
+        er += 1
+ct += 1
+er = er / ct
+print(er)
+er_k = []
+
+for i in range(1, k_max + 1):
+    er_k.append(er)
+
+graph_def(k_max, er_k, sweet, crunch, start_Data, colours, class_s_info)
+graph.show()
+print("===============================================================")