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fix: converted single to triple backticks11 (#36238)

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Randell Dawson
2019-06-20 13:42:13 -07:00
committed by Tom
parent 397014136e
commit 54d303ce1f
75 changed files with 1673 additions and 1430 deletions
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77
guide/arabic/machine-learning/clustering-algorithms/index.md
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@@ -60,44 +60,45 @@ localeTitle: خوارزميات التجميع
في ما يلي مثال تجميع في Python يستخدم " [مجموعة بيانات Iris"](https://www.kaggle.com/uciml/iris)
`import pandas as pd
import numpy as np
iris = pd.read_csv('Iris.csv')
del iris['Id']
del iris['SepalLengthCm']
del iris['SepalWidthCm']
from matplotlib import pyplot as plt
# k is the input parameter set to the number of species
k = len(iris['Species'].unique())
for i in iris['Species'].unique():
# select only the applicable rows
ds = iris[iris['Species'] == i]
# plot the points
plt.plot(ds[['PetalLengthCm']],ds[['PetalWidthCm']],'o')
plt.title("Original Iris by Species")
plt.show()
from sklearn import cluster
del iris['Species']
kmeans = cluster.KMeans(n_clusters=k, n_init=10, max_iter=300, algorithm='auto')
kmeans.fit(iris)
labels = kmeans.labels_
centroids = kmeans.cluster_centers_
for i in range(k):
# select only data observations from the applicable cluster
ds = iris.iloc[np.where(labels==i)]
# plot the data observations
plt.plot(ds['PetalLengthCm'],ds['PetalWidthCm'],'o')
# plot the centroids
lines = plt.plot(centroids[i,0],centroids[i,1],'kx')
# make the centroid x's bigger
plt.setp(lines,ms=15.0)
plt.setp(lines,mew=2.0)
plt.title("Iris by K-Means Clustering")
plt.show()
`
```python
import pandas as pd
import numpy as np
iris = pd.read_csv('Iris.csv')
del iris['Id']
del iris['SepalLengthCm']
del iris['SepalWidthCm']
from matplotlib import pyplot as plt
# k is the input parameter set to the number of species
k = len(iris['Species'].unique())
for i in iris['Species'].unique():
# select only the applicable rows
ds = iris[iris['Species'] == i]
# plot the points
plt.plot(ds[['PetalLengthCm']],ds[['PetalWidthCm']],'o')
plt.title("Original Iris by Species")
plt.show()
from sklearn import cluster
del iris['Species']
kmeans = cluster.KMeans(n_clusters=k, n_init=10, max_iter=300, algorithm='auto')
kmeans.fit(iris)
labels = kmeans.labels_
centroids = kmeans.cluster_centers_
for i in range(k):
# select only data observations from the applicable cluster
ds = iris.iloc[np.where(labels==i)]
# plot the data observations
plt.plot(ds['PetalLengthCm'],ds['PetalWidthCm'],'o')
# plot the centroids
lines = plt.plot(centroids[i,0],centroids[i,1],'kx')
# make the centroid x's bigger
plt.setp(lines,ms=15.0)
plt.setp(lines,mew=2.0)
plt.title("Iris by K-Means Clustering")
plt.show()
```
بما أن نقاط البيانات تنتمي عادة إلى مساحة عالية الأبعاد ، فإن مقياس التشابه غالباً ما يتم تعريفه على أنه مسافة بين متجهين (Euclidean ، Manhathan ، Cosine ، Mahalanobis…)