# 尋找離樣本最近的鄰居
from sklearn import datasets
from sklearn.neighbors import nearestneighbors
from sklearn.preprocessing import standardscaler
# 載入資料
iris = datasets.load_iris(
)features = iris.data
# 標準化資料
standardizer = standardscaler(
)# features 特徵標準化
features_standardized = standardizer.fit_transform(features)
nearest_neighbors = nearestneighbors(n_neighbors=2)
.fit(features_standardized)
#nearest_neighbors_euclidian = nearestneighbors(n_neighbors=2, metric='euclidian').fit(features_standardized)
# 建立測試資料
new_observation =[1
,1,1
,1]
# 獲取最近兩個點的索引，距離
distances, indices = nearest_neighbors.kneighbors(
[new_observation])
# features_standardized[indices] 距離最近的兩個值
indices
# 距離
distances
features_standardized[indices]
array([[
[1.03800476
,0.55861082
,1.10378283
,1.18556721],
[0.79566902
,0.32841405
,0.76275827
,1.05393502]]
])# metric 設定距離指標
nearestneighbors_euclidean = nearestneighbors(
n_neighbors=
2, metric=
'euclidean'
).fit(features_standardized)
# 檢視距離
distances
array([[
0.49140089
,0.74294782]]
)# 尋找最近的3個點
nearestneighbors_euclidean = nearestneighbors(
n_neighbors=
3, metric=
"euclidean"
).fit(features_standardized)
nearestneighbors_euclidean
# kneighbors_graph 建立乙個矩陣,表示離每個觀察值最近的點
# 包含每個觀察值和離他最近的3個鄰居
nearest_neighbors_with_self = nearestneighbors_euclidean.kneighbors_graph(
features_standardized)
.toarray()
# type(nearest_neighbors_with_self)
nearest_neighbors_with_self
list
(enumerate
(nearest_neighbors_with_self)
)# 從最近鄰居的列表移自己
for i, x in
enumerate
(nearest_neighbors_with_self)
: x[i]=0
nearest_neighbors_with_self
# 檢視裡第乙個樣本最近的兩個鄰居
nearest_neighbors_with_self[0]
array([0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,1.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
1.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
,0.,
0.,0
.,0.
])
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