From 81097624a50ff65d9eae514854e633fb930f547f Mon Sep 17 00:00:00 2001
From: "MSI\\alber" <alberto.cavallo@synesthesia.it>
Date: Wed, 25 Oct 2023 17:10:51 +0200
Subject: [PATCH] 25/10 It seems working but accuracy 0.35
---
knn.py | 53 +++++++++++++++++++++++++++++++++++++++++++--------
read_cifar.py | 22 +--------------------
2 files changed, 46 insertions(+), 29 deletions(-)
diff --git a/knn.py b/knn.py
index 870f2a7..370e49b 100644
--- a/knn.py
+++ b/knn.py
@@ -1,15 +1,15 @@
-import numpy as np
+from read_cifar import *
+from collections import Counter
def compute_distance(m1, m2):
if m1.shape != m2.shape:
raise ValueError("Dimensions must be identical")
-
+ #distance = np.linalg.norm(m1 - m2)
x = (m1 - m2) ** 2
y = np.sum(x)
dist = np.sqrt(y)
return dist
-
def distance_matrix(data_train, data_test):
dists = []
@@ -21,11 +21,48 @@ def distance_matrix(data_train, data_test):
return dists
+def knn_predict(dists, labels_train, k):
+ predictions=[]
+ for distances in dists:
+ min_indexes = np.argpartition(distances, k)[:k]
+ possible_pred = labels_train[min_indexes]
+ counted = Counter(possible_pred)
+ pred = counted.most_common(1)[0][0]
+ predictions.append(pred)
+ return predictions
- return dists
+def evaluate_knn(predictions, labels_test):
+ sum=0
+ for i in range(len(predictions)):
+ if predictions[i] == labels_test[i]:
+ sum+=1
+
+ return sum / len(predictions)
+
+'''def evaluate_knn(data_train , labels_train,data_test ,labels_test, k):
+
+ return'''
+
+def main():
+ folder_path = 'data/cifar-10-batches-py'
+ data, labels = read_cifar(folder_path)
+
+ print((data.shape))
+ print((labels.shape))
+
+ data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
+
+ print("Training set shape:", data_train.shape, labels_train.shape)
+ print("Testing set shape:", data_test.shape, labels_test.shape)
+
+ dists=distance_matrix(data_train, data_test)
+
+ prediction=knn_predict(dists, labels_train, 4)
+
+ accuracy = evaluate_knn(prediction, labels_test)
+
+ print(accuracy)
-def knn_predict(dist, labels_train, k):
- return
+if __name__ == "__main__":
+ main()
-def evaluate_knn(data_train , labels_train,data_test ,labels_test, k):
- return
\ No newline at end of file
diff --git a/read_cifar.py b/read_cifar.py
index 97bf59b..fb1251f 100644
--- a/read_cifar.py
+++ b/read_cifar.py
@@ -3,7 +3,6 @@ import numpy as np
import os
from sklearn.model_selection import train_test_split
-
def read_cifar_batch(batch):
with open(batch, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
@@ -21,30 +20,11 @@ def read_cifar(path):
data_batch, labels_batch = read_cifar_batch(path + '/' + batch)
data.append(data_batch)
labels.append(labels_batch)
- return np.array(data, dtype=np.float32).reshape((60000,3072)), np.array(labels, dtype=np.int64).reshape(-1)
+ return np.array(data, dtype=np.float32).reshape((60000, 3072)), np.array(labels, dtype=np.int64).reshape(-1)
def split_dataset(data, labels, split):
data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=1-split, shuffle=True)
-
return data_train, data_test, labels_train, labels_test
-def main():
- folder_path = 'data/cifar-10-batches-py'
- data, labels = read_cifar(folder_path)
-
- print((data.shape))
- print((labels.shape))
-
- data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
-
- print("Training set shape:", data_train.shape, labels_train.shape)
- print("Testing set shape:", data_test.shape, labels_test.shape)
-
-
-
-
-
-if __name__ == "__main__":
- main()
--
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