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Wrap lines that go beyond GitHub Editor (#1925)

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* Wrap lines that go beyond GiHub Editor

* flake8 --count --select=E501 --max-line-length=127

* updating DIRECTORY.md

* Update strassen_matrix_multiplication.py

* fixup! Format Python code with psf/black push

* Update decision_tree.py

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
This commit is contained in:
Christian Clauss
2020-05-01 23:36:35 +02:00
committed by GitHub
parent bcaa88b26c
commit 6acd7fb5ce
19 changed files with 161 additions and 82 deletions
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12
machine_learning/support_vector_machines.py
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@@ -3,6 +3,7 @@ from sklearn import svm
from sklearn.model_selection import train_test_split
import doctest
# different functions implementing different types of SVM's
def NuSVC(train_x, train_y):
svc_NuSVC = svm.NuSVC()
@@ -17,8 +18,11 @@ def Linearsvc(train_x, train_y):
def SVC(train_x, train_y):
# svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None)
# various parameters like "kernel","gamma","C" can effectively tuned for a given machine learning model.
# svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True,
# probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False,
# max_iter=-1, random_state=None)
# various parameters like "kernel","gamma","C" can effectively tuned for a given
# machine learning model.
SVC = svm.SVC(gamma="auto")
SVC.fit(train_x, train_y)
return SVC
@@ -27,8 +31,8 @@ def SVC(train_x, train_y):
def test(X_new):
"""
3 test cases to be passed
an array containing the sepal length (cm), sepal width (cm),petal length (cm),petal width (cm)
based on which the target name will be predicted
an array containing the sepal length (cm), sepal width (cm), petal length (cm),
petal width (cm) based on which the target name will be predicted
>>> test([1,2,1,4])
'virginica'
>>> test([5, 2, 4, 1])