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El Alimi Sara
Image Classification
Commits
efa3fceb
Commit
efa3fceb
authored
1 year ago
by
selalimi
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knn.py
+27
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27 additions, 30 deletions
knn.py
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27 additions
and
30 deletions
knn.py
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27
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30
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efa3fceb
...
...
@@ -5,16 +5,15 @@ import matplotlib.pyplot as plt
import
plotly.graph_objects
as
go
# Commentaire global expliquant le but du code
'''
Here is the code to compute the L2 Euclidean distance matrix and predict labels using k-nearest neighbors:
'''
# Create distance Matrix
'''
Arguments:
-
Deux
matrices.
-
Two
matrices.
Returns:
dists
:
la matrice de distances euclidiennes L2
.
La
computation
de cette
f
o
nction
doit être effectuée uniquement avec des
manipulations
de matrices
.
dists:
the L2 Euclidean distance matrix
.
The
computation
of this
f
u
nction
should be done solely through matrix
manipulations.
'''
def
distance_matrix
(
X
,
Y
):
XX
=
np
.
sum
(
X
**
2
,
axis
=
1
,
keepdims
=
True
)
...
...
@@ -26,12 +25,12 @@ def distance_matrix(X, Y):
# KNN predict
'''
Arguments:
-dists
:
la matrice de distances entre l
'
ensemble d
'
entraînement et l
'
ensemble d
e test.
-labels_train
:
les étiquettes d
'
entraînement
.
- k
:
l
e n
o
mb
re de voisin
s.
-
dists:
the distance matrix between the training set and th
e test
set
.
-
labels_train:
training labels
.
- k:
th
e n
u
mb
er of neighbor
s.
Returns:
-
Les étiquettes prédites pour les élé
ments
de
data_test.
-
Predicted labels for the ele
ments
in
data_test.
'''
def
knn_predict
(
dists
,
labels_train
,
k
):
n_test
=
dists
.
shape
[
0
]
...
...
@@ -46,15 +45,14 @@ def knn_predict(dists, labels_train, k):
'''
Here is the code to evaluate k-nearest neighbors and plot the accuracy as a function of k:
'''
'''
Arguments:
-data_train
:
les données d
'
entraînement
.
-labels_train
:
les étiquettes
correspond
ante
s.
-data_test
:
les donné
es d
e test
.
-labels_test
:
les étiquettes
correspond
ante
s.
-
k : l
e n
o
mb
re de voisin
s.
-
data_train:
training data
.
-
labels_train: correspond
ing label
s.
-
data_test:
t
es
t
d
ata
.
-
labels_test: correspond
ing label
s.
-
k: th
e n
u
mb
er of neighbor
s.
Returns:
-La précision du modèle Knn : le taux de classification entre les valeurs prédites et les observations
réelles des données de test.
- Accuracy of the Knn model: the classification rate between predicted values and actual observations from test data.
'''
def
evaluate_knn
(
data_train
,
labels_train
,
data_test
,
labels_test
,
k
):
dists
=
distance_matrix
(
data_test
,
data_train
)
...
...
@@ -63,13 +61,13 @@ def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
return
accuracy
# Plot Accuracy of KNN model
'''
The function plots the variation of accuracy with the number of neighbors K.
'''
'''
******La fonction trace la variation de la précision en fonction du nombre de voisins K****
Arguments:
-X_train
:
données d
'
entraînement
-y_train
:
étiquettes d
'
entraînement
-X_test
:
donné
es d
e test
-y_test
:
étiquettes de test
-
X_train:
training data.
-
y_train:
training labels.
-
X_test:
t
es
t
d
ata.
-
y_test:
test labels.
'''
def
plot_KNN
(
X_train
,
y_train
,
X_test
,
y_test
,
max_k
=
20
):
neighbors
=
np
.
arange
(
1
,
max_k
+
1
)
...
...
@@ -79,4 +77,3 @@ def plot_KNN(X_train, y_train, X_test, y_test, max_k=20):
plt
.
ylabel
(
'
Accuracy
'
)
plt
.
title
(
'
Variation of Accuracy with K
'
)
plt
.
savefig
(
"
Results/knn.png
"
)
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