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Code: Alles auswählen

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn import preprocessing
import pandas
import scipy.spatial
import scipy.stats
import scipy
import random
import numpy

path = "..."

Examples = pandas.read_excel(io=path, sheet_name=0, header=None, skiprows=2)
Examples = Examples.values

print(Examples)
>>> [Out]
[[ 2.    2.1 ]
 [ 3.    4.9 ]
 [ 4.    1.4 ]
 [ 3.4   2.1 ]
 [ 5.    1.9 ]
 [ 6.7   2.8 ]
 [55.   70.  ]
 [63.   64.3 ]
 [65.   54.5 ]
 [54.   80.  ]
 [42.   65.  ]
 [55.   65.  ]
 [38.   45.  ]
 [ 2.44  5.  ]
 [43.   32.  ]
 [70.   50.  ]]


train_x = Examples[0:13,0].reshape(-1,1)
train_y = Examples[0:13,1].reshape(-1,1)
test_x = Examples[13:16,0].reshape(-1,1)
test_y = Examples[13:16,1].reshape(-1,1)

#Encoden
lab_enc = preprocessing.LabelEncoder()
training_y_encoded = lab_enc.fit_transform(train_y.ravel())

#Model
clf = LogisticRegression()
clf.fit(train_x, training_y_encoded)

#Koeffizienten
coef = clf.coef_
print(coef)
>>> [Out]
[[-0.30020469]
 [-0.20023317]
 [-0.34766145]
 [-0.11155487]
 [-0.41948743]
 [-0.02386848]
 [-0.00537448]
 [-0.0065249 ]
 [ 0.00056065]
 [-0.01138778]
 [-0.01202993]]
 
 print('intercept:', clf.intercept_.reshape(-1,1))
 >> [Out]
 [[-0.21955754]
 [-0.4051622 ]
 [ 0.35798376]
 [-0.59208539]
 [-0.02204233]
 [-1.06375401]
 [-1.35218985]
 [-1.33102731]
 [-1.15507754]
 [-1.24631744]
 [-1.23570657]]

Code: Alles auswählen

train_x = Examples[0:13,0].reshape(-1,1)
train_y = Examples[0:13,1]
test_x = Examples[13:16,0].reshape(-1,1)
test_y = Examples[13:16,1]
 
#Model
clf = LinearRegression()
clf.fit(train_x, train_y)

#Koeffizienten
print(clf.coef_)
print(clf.intercept_)

[1.17162974]  <--- Steigung
-0.3909647450016891   <--- y-Intercept (x=0)