Initial commit

KNN.py

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+from math import sqrt
+import pandas as pd
+import numpy as np
+from collections import Counter
+
+
+def trainTestSplit(data):
+    rowNum = data.shape[0]
+    splitIndex = int(80 / 100 * rowNum)
+    train = data.iloc[:splitIndex].reset_index(drop=True) #reset indexes to start from index 0
+    test = data.iloc[splitIndex:].reset_index(drop=True)
+    return train, test
+
+def eucledianDistance(p1, p2):
+    distance = np.sqrt(np.sum((p1 - p2) ** 2))
+    return distance
+
+
+
+df = pd.read_csv('wine.csv').sample(frac=1)
+
+classNum = len(set(df.iloc[:, 0]))
+trainData, testData = trainTestSplit(df)
+
+featuresTrain = trainData.iloc[:, 1:].values
+labelsTrain = trainData.iloc[:, 0].values
+
+trainNum = len(featuresTrain)
+kk = sqrt(trainNum)
+
+
+featuresTest = testData.iloc[:, 1:].values
+labelsTest = testData.iloc[:, 0].values
+
+# print(labels)
+
+def KNNPredict(featuresTrain, labelsTrain, featureTest, k=15):
+    distances = []
+
+    #for every example in the training set, calculate eucledien distance against the test example
+    for i, point in enumerate(featuresTrain):
+        distances.append((i, eucledianDistance(featureTest, point)))
+    distances.sort(key = lambda x : x[1])
+
+    labels = []
+    for i, distance in distances[:k]:
+        labels.append(labelsTrain[i])
+
+    count = Counter(labels)
+    label = count.most_common()[0][0]
+
+    return label
+
+def confusionMatrix(predictions, labels, classNum):
+    mat = np.zeros((classNum, classNum), dtype=np.int32)
+
+    for i in range(len(predictions)):
+        mat[predictions[i] - 1, labels[i] - 1] += 1
+
+    return mat
+
+predictions = []
+for point in featuresTest:
+    predictions.append(KNNPredict(featuresTrain, labelsTrain, point))
+accuracy = np.sum(predictions == labelsTest) / len(labelsTest) * 100
+print(accuracy)
+print(confusionMatrix(predictions, labelsTest, classNum))