def PLS_Canonical(csv_data,
point_index,
sub_index,
var_name,
train=None,
components=None):
X_array = []
temp_array = []
for j in csv_data:
temp_array = j[point_index - 1:point_index + 8]
X_array.append(temp_array)
X_array = np.array(X_array)
if components == None:
components = np.shape(X_array)[1]
for i in range(7):
Y_array = np.array(csv_data[:, sub_index - 1 + i])
plsca = PLSCanonical(n_components=1)
plsca.fit(X_array, Y_array)
print(var_name[sub_index + i])
print("R^2 =", np.around(plsca.score(X_array, Y_array), decimals=2))
#correct not accurate
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
from sklearn.svm import SVC
import numpy as np
import pandas as pd
from sklearn.cross_decomposition import PLSRegression
from sklearn.cross_decomposition import PLSCanonical
df=pd.read_csv('newdata.csv')
x=df.drop(['tag'],axis=1)
y=df.drop(['kx','ky','kz','wa','wb','wc','wd','we','wf'],axis=1)
X_train , X_test , Y_train , Y_test = train_test_split(x,y , random_state=5)
plsr=PLSRegression()
plsr.fit(X_train,Y_train)
plsc=PLSCanonical()
plsc.fit(X_train,Y_train)
print (plsr.score(X_test,Y_test))
print (plsc.score(X_test,Y_test))
#correct not accurate
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
from sklearn.svm import SVC
import numpy as np
import pandas as pd
from sklearn.cross_decomposition import PLSRegression
from sklearn.cross_decomposition import PLSCanonical
df = pd.read_csv('newdata.csv')
x = df.drop(['tag'], axis=1)
y = df.drop(['kx', 'ky', 'kz', 'wa', 'wb', 'wc', 'wd', 'we', 'wf'], axis=1)
X_train, X_test, Y_train, Y_test = train_test_split(x, y, random_state=5)
plsr = PLSRegression()
plsr.fit(X_train, Y_train)
plsc = PLSCanonical()
plsc.fit(X_train, Y_train)
print(plsr.score(X_test, Y_test))
print(plsc.score(X_test, Y_test))
