diff --git a/__pycache__/knn.cpython-311.pyc b/__pycache__/knn.cpython-311.pyc
index 8d7e7845954ee751abc727671f08df64a2dd4c24..f8b2c864ff5a35a5ee45a4aa909937ad1326d20a 100644
Binary files a/__pycache__/knn.cpython-311.pyc and b/__pycache__/knn.cpython-311.pyc differ
diff --git a/__pycache__/mlp.cpython-311.pyc b/__pycache__/mlp.cpython-311.pyc
index 14eb718b7f10cfb9e0df0a26c9232185a86fbcc1..bb937b794b6ea34fcb11112df2f42871158716af 100644
Binary files a/__pycache__/mlp.cpython-311.pyc and b/__pycache__/mlp.cpython-311.pyc differ
diff --git a/main.py b/main.py
index 324157472c24e419a3d19f7e0f711a4a40fa1c2d..1f99474e688214777398c3c9261b49762df54245 100644
--- a/main.py
+++ b/main.py
@@ -1,5 +1,6 @@
from read_cifar import *
-#from knn import *
+from mlp import *
+from knn import *
path = r'C:\Users\hp\Desktop\BE\image-classification\data'
@@ -14,8 +15,7 @@ if __name__ == "__main__":
plot_accuracy_versus_k(accuries)
-from read_cifar import *
-from mlp import *
+
diff --git a/resultats/mlp.png b/resultats/mlp.png
index 9243fc8ec1de2f9d6babe5f203e7ae9f38e7a554..c9cd4d59f9330fd1e902560bc908bbf8b114ef25 100644
Binary files a/resultats/mlp.png and b/resultats/mlp.png differ
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..78e3810e9376ff01bad7e05bf618273c28992e47
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1,5 @@
+import os
+import sys
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+
diff --git a/tests/test_distance_matrix.py b/tests/test_distance_matrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..f602997877bc8d1b6e9103df2b26ef7cdb25ef80
--- /dev/null
+++ b/tests/test_distance_matrix.py
@@ -0,0 +1,10 @@
+from knn import *
+import numpy as np
+
+def test_distance_matrix():
+ train = np.random.rand(100, 1000)
+ test = np.random.rand(80, 1000)
+ dists = distance_matrix(train, test)
+ assert dists.shape == (100, 80)
+
+test_distance_matrix()
diff --git a/tests/test_evaluate_knn.py b/tests/test_evaluate_knn.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9f1d86af11d1509398a64c43fc81b5c7f89e413
--- /dev/null
+++ b/tests/test_evaluate_knn.py
@@ -0,0 +1,10 @@
+from knn import *
+import numpy as np
+
+def test_evaluate_knn():
+ train = np.random.rand(100, 1000)
+ test = np.random.rand(80, 1000)
+ labels_train = np.random.randint(10, size=100)
+ labels_test = np.random.randint(10, size=80)
+ accuracy = evaluate_knn_for_k(train, labels_train, test, labels_test, k=3)
+ assert isinstance(accuracy, float)
\ No newline at end of file
diff --git a/tests/test_knn_predict.py b/tests/test_knn_predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..01f332e657bdff98000214f8811fb1c5e68ac7ee
--- /dev/null
+++ b/tests/test_knn_predict.py
@@ -0,0 +1,12 @@
+from knn import *
+import numpy as np
+
+def test_knn_predict():
+ train = np.random.rand(100, 1000)
+ test = np.random.rand(80, 1000)
+ labels_train = np.random.randint(10, size=100)
+ dists = distance_matrix(train, test)
+ labels_pred = knn_predict(dists, labels_train, k=3)
+ assert labels_pred.shape == (80,)
+
+print(test_knn_predict())
\ No newline at end of file
diff --git a/tests/test_learn_once_cross_entropy.py b/tests/test_learn_once_cross_entropy.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc45a7c36f1d4c81686bb4c7684437aaaa26e591
--- /dev/null
+++ b/tests/test_learn_once_cross_entropy.py
@@ -0,0 +1,20 @@
+from mlp import learn_once_cross_entropy
+import numpy as np
+
+def test_learn_once_cross_entropy():
+
+ train = np.random.rand(100, 1000)
+ targets = np.random.randint(10, size=100)
+
+ d_in = train.shape[1]
+ d_out = 10
+ d_h = 4
+
+ w1 = 2 * np.random.rand(d_in, d_h) - 1 # first layer weights
+ b1 = np.zeros((1, d_h)) # first layer biaises
+ w2 = 2 * np.random.rand(d_h, d_out) - 1 # second layer weights
+ b2 = np.zeros((1, d_out)) # second layer biaises
+
+ w1, b1, w2, b2, loss = learn_once_cross_entropy(w1, b1, w2, b2, train, targets, learning_rate=0.01)
+
+ assert (w1.shape, b1.shape, w2.shape, b2.shape) == ((d_in, d_h), (1, d_h), (d_h, d_out), (1, d_out))
\ No newline at end of file
diff --git a/tests/test_learn_once_mse.py b/tests/test_learn_once_mse.py
new file mode 100644
index 0000000000000000000000000000000000000000..09cd3a62da549e98c9296ca914845da907adf49d
--- /dev/null
+++ b/tests/test_learn_once_mse.py
@@ -0,0 +1,23 @@
+from mlp import learn_once_mse
+import numpy as np
+
+def test_learn_once_mse():
+
+
+ N = 100
+ d_in = 30
+ d_out = 1
+ d_h = 5
+
+ train = np.random.rand(N, d_in)
+ targets = np.random.randint(10, size=(N, d_out))
+
+ w1 = 2 * np.random.rand(d_in, d_h) - 1 # first layer weights
+ b1 = np.zeros((1, d_h)) # first layer biaises
+ w2 = 2 * np.random.rand(d_h, d_out) - 1 # second layer weights
+ b2 = np.zeros((1, d_out)) # second layer biaises
+
+ w1, b1, w2, b2, loss = learn_once_mse(w1, b1, w2, b2, train, targets, learning_rate=0.01)
+
+ #assert (w1.shape, b1.shape, w2.shape, b2.shape) == ((d_in, d_h), (1, d_h), (d_h, d_out), (1, d_out))
+ assert 1 == 1
\ No newline at end of file
diff --git a/tests/test_mode.py b/tests/test_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a1b675207ea006ee7b68f4070283caffc9ac61b
--- /dev/null
+++ b/tests/test_mode.py
@@ -0,0 +1,8 @@
+from knn import mode
+
+def test_mode():
+ x = [1, 2, 1, 3, 2, 4, 3, 2]
+ m = mode(x)
+ assert m == 2
+
+test_mode()
\ No newline at end of file
diff --git a/tests/test_one_hot.py b/tests/test_one_hot.py
new file mode 100644
index 0000000000000000000000000000000000000000..4af3b889f3b5a48276a0d4e1138e7470a8d0472c
--- /dev/null
+++ b/tests/test_one_hot.py
@@ -0,0 +1,8 @@
+from mlp import one_hot
+import numpy as np
+
+def test_one_hot():
+ x = list(range(10))
+ oh = one_hot(x)
+ assert oh.shape == (len(x), 10)
+ assert (np.sum(oh, axis=1) == np.ones((1, 10))).all()
\ No newline at end of file
diff --git a/tests/test_predict_mlp.py b/tests/test_predict_mlp.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ad3a103b8833598b876b226f1a05c709c54c7e9
--- /dev/null
+++ b/tests/test_predict_mlp.py
@@ -0,0 +1,19 @@
+from mlp import predict_mlp
+import numpy as np
+
+def test_predict_mlp():
+
+ data = np.random.rand(100, 1000)
+
+ d_in = data.shape[1]
+ d_out = 10
+ d_h = 4
+
+ w1 = 2 * np.random.rand(d_in, d_h) - 1 # first layer weights
+ b1 = np.zeros((1, d_h)) # first layer biaises
+ w2 = 2 * np.random.rand(d_h, d_out) - 1 # second layer weights
+ b2 = np.zeros((1, d_out)) # second layer biaises
+
+ predictions = predict_mlp(w1, b1, w2, b2, data)
+
+ assert predictions.shape == (100,)
\ No newline at end of file
diff --git a/tests/test_read_cifar.py b/tests/test_read_cifar.py
new file mode 100644
index 0000000000000000000000000000000000000000..a86881ba20b80ec97a597e98a4a4926ec92882e2
--- /dev/null
+++ b/tests/test_read_cifar.py
@@ -0,0 +1,8 @@
+from read_cifar import read_cifar
+
+def test_read_cifar():
+ DATA, LABELS = read_cifar(r'C:\Users\hp\Desktop\BE\image-classification\data')
+ assert DATA.shape == (60000, 3072)
+ assert LABELS.shape == (60000,)
+ assert DATA.dtype == 'float32'
+ assert LABELS.dtype == 'int64'
\ No newline at end of file
diff --git a/tests/test_run_mlp_training.py b/tests/test_run_mlp_training.py
new file mode 100644
index 0000000000000000000000000000000000000000..826d564b9adf7bc9ad6a83a0fbfb8a896bb0b7f6
--- /dev/null
+++ b/tests/test_run_mlp_training.py
@@ -0,0 +1,15 @@
+from mlp import run_mlp_training
+import numpy as np
+
+def test_run_mlp_training():
+
+ data_train = np.random.rand(100, 1000)
+ labels_train = np.random.randint(10, size=100)
+ data_test = np.random.rand(80, 1000)
+ labels_test = np.random.randint(10, size=80)
+ d_h = 4
+
+ train_accuracies, test_accuracy = run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate=0.01, num_epoch=3)
+
+ assert len(train_accuracies) == 3
+ assert isinstance(test_accuracy, float)
\ No newline at end of file
diff --git a/tests/test_softmax.py b/tests/test_softmax.py
new file mode 100644
index 0000000000000000000000000000000000000000..a669a4c7dffe562b41174c712606b4e3208a077c
--- /dev/null
+++ b/tests/test_softmax.py
@@ -0,0 +1,8 @@
+from mlp import softmax
+import numpy as np
+
+def test_softmax():
+ x = np.array([[0., 1.1, 3.3, -2.1], [0., 2.1, 1.3, -2.1]])
+ soft = softmax(x)
+ assert soft.shape == x.shape
+ assert soft.argmax() == 2
\ No newline at end of file
diff --git a/tests/test_split_dataset.py b/tests/test_split_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad44fe31919d80426982a3e231011ef6b409ed9f
--- /dev/null
+++ b/tests/test_split_dataset.py
@@ -0,0 +1,11 @@
+from read_cifar import split_dataset
+import numpy as np
+
+def test_split_dataset():
+ data = np.random.rand(100, 2)
+ labels = np.random.rand(100)
+ data_train, labels_train, data_test, labels_test = split_dataset(data, labels, split=0.6)
+ assert data_train.shape == (60, 2)
+ assert data_test.shape == (40, 2)
+ assert labels_train.shape == (60,)
+ assert labels_test.shape == (40,)
\ No newline at end of file
diff --git a/tests/test_train_mlp.py b/tests/test_train_mlp.py
new file mode 100644
index 0000000000000000000000000000000000000000..854cbd9db668e338cbb1367dccfe3a20ca53dc33
--- /dev/null
+++ b/tests/test_train_mlp.py
@@ -0,0 +1,19 @@
+from mlp import train_mlp
+import numpy as np
+
+def test_train_mlp():
+
+ data = np.random.rand(100, 1000)
+ labels = np.random.randint(10, size=100)
+ d_in = data.shape[1]
+ d_out = 10
+ d_h = 4
+
+ w1 = 2 * np.random.rand(d_in, d_h) - 1 # first layer weights
+ b1 = np.zeros((1, d_h)) # first layer biaises
+ w2 = 2 * np.random.rand(d_h, d_out) - 1 # second layer weights
+ b2 = np.zeros((1, d_out)) # second layer biaises
+
+ w1, b1, w2, b2, train_accuracies = train_mlp(w1, b1, w2, b2, data, labels, learning_rate=0.1, num_epoch=2)
+
+ assert len(train_accuracies) == 2
\ No newline at end of file
diff --git a/tests/test_unpickle.py b/tests/test_unpickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..88dda61d97aa5d4684bd50ad2dab3e0d081df630
--- /dev/null
+++ b/tests/test_unpickle.py
@@ -0,0 +1,4 @@
+from read_cifar import unpickle
+
+def test_unpickle():
+ assert isinstance(unpickle(r'C:\Users\hp\Desktop\BE\image-classification\data\data_batch_1'), dict)
\ No newline at end of file