from KNN_1_data import result,data,predict
# https://docs.microsoft.com/en-us/windows/win32/fileio/maximum-file-path-limitation?tabs=cmd
command = '''

pip3 install sklearn==0.0
cd "C:/Users/ivan.lee.PRIMECREATION/Documents/ivan/Projects source/Others/h/Ra_calculate"
python3 KNN_1.py


'''

# X = [[0], [1], [2], [3]]
# y = [0, 0, 1, 1]

from sklearn.neighbors import KNeighborsRegressor

neigh = KNeighborsRegressor(n_neighbors=2)
print(len(data),len(result))
neigh.fit(data, result)
# KNeighborsRegressor(...)
# r1 = neigh.predict(data)
r1 = neigh.predict(predict)
for x in r1:
	print(x)
# [0.5]
c9770b2fb04b4130be787914f34360a7d1fa670a
<mask token>

<mask token>
print(len(data), len(result))
neigh.fit(data, result)
<mask token>
for x in r1:
    print(x)

<mask token>
command = """

pip3 install sklearn==0.0
cd "C:/Users/ivan.lee.PRIMECREATION/Documents/ivan/Projects source/Others/h/Ra_calculate"
python3 KNN_1.py


"""
<mask token>
neigh = KNeighborsRegressor(n_neighbors=2)
print(len(data), len(result))
neigh.fit(data, result)
r1 = neigh.predict(predict)
for x in r1:
    print(x)

from KNN_1_data import result, data, predict
command = """

pip3 install sklearn==0.0
cd "C:/Users/ivan.lee.PRIMECREATION/Documents/ivan/Projects source/Others/h/Ra_calculate"
python3 KNN_1.py


"""
from sklearn.neighbors import KNeighborsRegressor
neigh = KNeighborsRegressor(n_neighbors=2)
print(len(data), len(result))
neigh.fit(data, result)
r1 = neigh.predict(predict)
for x in r1:
    print(x)

from KNN_1_data import result,data,predict
# https://docs.microsoft.com/en-us/windows/win32/fileio/maximum-file-path-limitation?tabs=cmd
command = '''

pip3 install sklearn==0.0
cd "C:/Users/ivan.lee.PRIMECREATION/Documents/ivan/Projects source/Others/h/Ra_calculate"
python3 KNN_1.py


'''

# X = [[0], [1], [2], [3]]
# y = [0, 0, 1, 1]

from sklearn.neighbors import KNeighborsRegressor

neigh = KNeighborsRegressor(n_neighbors=2)
print(len(data),len(result))
neigh.fit(data, result)
# KNeighborsRegressor(...)
# r1 = neigh.predict(data)
r1 = neigh.predict(predict)
for x in r1:
	print(x)
# [0.5]