From 00589b68a3a6050f1c41c3499a7a3cd1be6efecb Mon Sep 17 00:00:00 2001
From: "MSI\\alber" <alberto.cavallo@synesthesia.it>
Date: Mon, 23 Oct 2023 16:31:14 +0200
Subject: [PATCH] Read cifar finished and distance matrix computed
---
knn.py | 19 +++++++++++++++++--
read_cifar.py | 42 +++++++++++++++++++++++++++++++++++++++---
2 files changed, 56 insertions(+), 5 deletions(-)
diff --git a/knn.py b/knn.py
index 1865cae..870f2a7 100644
--- a/knn.py
+++ b/knn.py
@@ -1,6 +1,6 @@
import numpy as np
-def distance_matrix(m1, m2):
+def compute_distance(m1, m2):
if m1.shape != m2.shape:
raise ValueError("Dimensions must be identical")
@@ -10,7 +10,22 @@ def distance_matrix(m1, m2):
return dist
-def knn_predict(dist, labels_train, k, ):
+def distance_matrix(data_train, data_test):
+ dists = []
+
+ for test in data_test:
+ dist = []
+ for train in data_train:
+ dist.append(compute_distance(test, train))
+ dists.append(dist)
+
+ return dists
+
+
+ return dists
+
+def knn_predict(dist, labels_train, k):
return
+
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 388de81..97bf59b 100644
--- a/read_cifar.py
+++ b/read_cifar.py
@@ -1,14 +1,50 @@
import pickle
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')
data = dict[b'data']
labels = dict[b'labels']
- return data.astype(np.float32), np.array(labels, dtype=np.int64)
+ print(dict[b'batch_label'])
+ return data, labels
+
+def read_cifar(path):
+ batches_list = os.listdir(path)
+ data, labels = [], []
+ for batch in batches_list:
+ if(batch == 'batches.meta' or batch == 'readme.html'):
+ continue
+ 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)
+
+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()
-batch='data/cifar-10-batches-py/data_batch_1'
-data, labels = read_cifar_batch(batch)
--
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