Create sklearn hello world example for Issue nogibjj#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