add regressor

demo/regressor_demo.py

@@ -0,0 +1,49 @@
+import matplotlib.pyplot as plt
+import neighbours as ns
+
+import numpy as np
+import random
+import math
+
+
+# function for generating a synthetic regression problem
+def f(x):
+    if x > 40:
+        return math.log(x, 2) - 6
+    else:
+        return math.cos(x * 0.1)
+
+
+# generate x coordinates
+X = [[i + random.uniform(-1, 1)] for i in np.arange(start=1, stop=100, step=1)]
+
+# calculate corresponding y coordinates
+y = [f(i[0]) + random.uniform(-0.1, 0.1) for i in X]
+
+# convert to numpy arrays
+X = np.array(X)
+y = np.array(y)
+
+# generate x coordinates for demo plot
+x_points = np.arange(start=0, stop=100, step=0.1)
+X_demo = np.array([[x] for x in x_points])
+
+# create a regressor then load data
+regressor = ns.KNNRegressor(1, 10, 7)
+regressor.load(X, y)
+
+# create an array to store predicted y values for demo plot
+y_predicted = []
+
+# get predictions for all samples in X_demo
+for sample in X_demo:
+    predicted_value = regressor.predict(sample, ns.distance.euclidean, ns.kernel.gaussian, 3)
+    y_predicted.append(predicted_value)
+
+# plot train points
+plt.plot(X, y, 'bo')
+
+# plot predicted y against x
+plt.plot(x_points, y_predicted, 'r')
+
+plt.show()