Create sklearn hello world example for Issue #9

mylib/sklearnlib.py

@@ -0,0 +1,31 @@
+"""
+sk-learn hello world demo
+"""
+from sklearn import linear_model
+
+def linearRegression(X, y, sample_weight=None):
+ """
+ Lineaer Regresssion based on sklearn
+
+ Parameters
+ ----------
+ X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ Training data.
+
+ y : array-like of shape (n_samples,) or (n_samples, n_targets)
+ Target values. Will be cast to X's dtype if necessary.
+
+ sample_weight : array-like of shape (n_samples,), default=None
+ Individual weights for each sample.
+
+ .. versionadded:: 0.17
+ parameter *sample_weight* support to LinearRegression.
+
+ Returns
+ -------
+ self : object
+ Fitted Estimator.
+ """
+ reg = linear_model.LinearRegression()
+ reg.fit(X, y, sample_weight)
+ return reg

test_main.py

@@ -4,6 +4,19 @@
 """
 
 from mylib.calculator import add
+import numpy as np
+from mylib.sklearnlib import linearRegression
+
 
 def test_add():
  assert add(1, 2) == 3
+
+
+def test_sklearn_linear_reg():
+ x = np.arange(10)
+ k, b = 2, 1
+ y = k * x + b
+ X = x[:, np.newaxis]
+ reg = linearRegression(X, y)
+ assert abs(reg.coef_[0] - k) < 1e-6
+ assert abs(reg.intercept_ - b) < 1e-6