Fixed Code Block Issue In README.md

README.md

@@ -139,20 +139,24 @@ in the prototypes directory.
 
 # How To Install
 
-To install and use this package first ensure you have pip installed and
-updated. Then execute the following code while in the packages main
-directory(directory containing setup.py):
-'''
+To install this package first ensure you have pip installed and
+updated. Then execute the following code while in the package's main
+directory(the directory containing setup.py):
+```
  pip install .
-'''
+```
 
-This will install k-nearest-neighbors into your current python 
-environment.
+This will install knn into your current python environment. You then
+will import knn.models to access classes for classification and 
+regression. knn.models also contains a mixin that provides nearest
+neighbor search methods you can use in your own classes.
 
-Note - Cython and Numpy are used in this project. Numpy is required to be
-installed but Cython is not. If Cython is not installed the packages .c 
+Note - Cython and Numpy are used in this project. Numpy is required but
+Cython is not. If Cython is not installed the packaged .c 
 files will be complied using your systems default c compiler. Numpy
-will be installed when executing the install code above.
+will be installed when installing knn.
+
+Note - This code was designed and tested using Python 3.5.2.
 
 
 

example_usage.py

@@ -1,7 +1,5 @@
 import numpy as np
-from knn.k_nearest_neighbors import KNNClassification, KNNRegression
-
-
+from knn.models import KNNClassification, KNNRegression
 
 '''
 Simple Example - Classification
@@ -25,178 +23,178 @@
 print("k=1 Predictions:\n" + str(predictions))
 
 
-# '''
-# MNIST Classification
-# '''
-# # Load Data
-# mnist_data = np.load('./sample_data/mnist/mnist_data.npz')
-# train_data = mnist_data['train_data']
-# test_data = mnist_data['test_data']
-#
-# # Subset Data If Desired
-# test_labels = test_data[:2000, 0]
-# test_data = test_data[:2000, 1:].astype(np.float)
-# train_labels = train_data[:6000, 0]
-# train_data = train_data[:6000, 1:].astype(np.float)
-#
-# # Create and Train Classifier
-# knn = KNNClassification(metric="manhattan")
-# knn.train(train_labels, train_data)
-#
-# # Get Predictions
-# predictions = knn.predict(test_data, k=5)
-#
-# print("Test Labels:\n" + str(test_labels))
-# print("Predicted Labels:\n" + str(predictions))
-#
-# accuracy = sum(test_labels == predictions)/test_labels.size
-# print("Accuracy: " + str(accuracy))
-#
-#
-# '''
-# Using A Distance Metric From Another Package
-# '''
-# from scipy.spatial import distance
-#
-# train_data = np.array([[1., 2., 3.],
-#                        [5., 6., 7.],
-#                        [8., 9., 10.]])
-# train_labels = np.array([0, 1, 1])
-#
-# test_data = np.array([[5., 10., 15.],
-#                       [10., 20., 30.]])
-#
-# # Needed To Swap Input Order And Set Metric Argument
-# def scipy_cityblock(vectors_a, vectors_b):
-#     return distance.cdist(vectors_b, vectors_a, 'cityblock')
-#
-# # Create and Train Classifier
-# knn = KNNClassification(metric=scipy_cityblock)
-# knn.train(train_labels, train_data)
-#
-# # Get Predictions
-# predictions = knn.predict(test_data, k=1)
-#
-# print("k=1 Predictions:\n" + str(predictions))
-#
-#
-# '''
-# Million Song Dataset(MSD) Regression
-# '''
-# msd_data = np.load('./sample_data/msd/msd_data.npz')
-#
-# # Note - astype() Used To Make Arrays Contiguous
-# train_data = msd_data['train_data']
-# train_response = train_data[:600000, 0]
-# train_data = train_data[:600000, 1:].astype(np.float)
-#
-# test_data = msd_data['test_data']
-# test_response = test_data[:500, 0]
-# test_data = test_data[:500, 1:].astype(np.float)
-#
-# knn = KNNRegression(metric="manhattan")
-# knn.train(train_response, train_data)
-#
-# # Get Predictions
-# predicted_response = knn.predict(test_data, k=35)
-#
-# print("Actual Response:\n" + str(test_response[:20]))
-# print("Predicted Response:\n" + str(predicted_response[:20]))
-#
-# error = np.sqrt(np.mean(np.square(test_response-predicted_response)))
-# print("Root Mean Square Error: " + str(error))
-#
-#
-# '''
-#  Simple Example - Classification Using Ball Tree
-# '''
-#
-# train_data = np.array([[4, -2], [5, 5], [8, 7], [-6, -1], [-1, -3], [-4,-8]]).astype(np.float)
-# train_labels = np.array([1, 1, 1, 0, 0, 0])
-#
-# test_data = np.array([[6, 4], [-8, -4]]).astype(np.float)
-#
-# knn = KNNClassification(metric="euclidean", use_tree=True, tree_leaf_size=3)
-# knn.train(train_labels, train_data)
-# predictions = knn.predict(test_data, k=3)
-# print("k=3 Predictions:\n" + str(predictions))
-#
-#
-# '''
-# MNIST Classification - Ball Tree
-# '''
-# # Load Data
-# mnist_data = np.load('./sample_data/mnist/mnist_data.npz')
-# train_data = mnist_data['train_data']
-# test_data = mnist_data['test_data']
-#
-# # Subset Data If Desired
-# test_labels = test_data[:1000, 0]
-# test_data = test_data[:1000, 1:].astype(np.float)
-# train_labels = train_data[:1000, 0]
-# train_data = train_data[:1000, 1:].astype(np.float)
-#
-# # Create and Train Classifier
-# knn = KNNClassification(metric="manhattan", use_tree=True, tree_leaf_size=100)
-# knn.train(train_labels, train_data)
-#
-# # Get Predictions
-# predictions = knn.predict(test_data, k=3)
-#
-# print("Test Labels:\n" + str(test_labels))
-# print("Predicted Labels:\n" + str(predictions))
-#
-# accuracy = sum(test_labels == predictions)/test_labels.size
-# print("Accuracy: " + str(accuracy))
-#
-#
-# '''
-# Million Song Dataset(MSD) Regression - Ball Tree
-# '''
-# msd_data = np.load('./sample_data/msd/msd_data.npz')
-#
-# train_data = msd_data['train_data']
-# train_response = train_data[:60000, 0]
-# train_data = train_data[:60000, 1:].astype(np.float)
-#
-# test_data = msd_data['test_data']
-# test_response = test_data[:500, 0]
-# test_data = test_data[:500, 1:].astype(np.float)
-#
-# knn = KNNRegression(metric="manhattan", use_tree=True, tree_leaf_size=3)
-# knn.train(train_response, train_data)
-#
-# # Get Predictions
-# predicted_response = knn.predict(test_data, k=35)
-#
-# print("Actual Response:\n" + str(test_response[:20]))
-# print("Predicted Response:\n" + str(predicted_response[:20]))
-#
-# error = np.sqrt(np.mean(np.square(test_response-predicted_response)))
-# print("Root Mean Square Error: " + str(error))
-#
-#
-# '''
-#  Structured Data
-# '''
-# # Ball Trees Benefit Heavily If Data Is Has Structure (Naturally Partitioned Into Hyper Spheres "Balls")
-#
-# points_per_region = 200
-# number_of_regions = 16
-#
-# random_data = np.empty((points_per_region * number_of_regions,2))
-# region = 0
-#
-# for i in range(-30, 30, 15):
-#     for j in range(-30, 30, 15):
-#         mu = np.array([i+7.5, j+7.5])
-#         sigma = np.array([[1, 0], [0, 1]])
-#         random_data[region:region+points_per_region] = np.random.multivariate_normal(mu, sigma, points_per_region)
-#         region += points_per_region
-#
-# labels = np.repeat(np.arange(number_of_regions), points_per_region)
-#
-# knn = KNNClassification(use_tree=True, tree_leaf_size=20, metric="euclidean")
-# knn.train(labels, random_data)
-# # Get Predictions
-# predictions = knn.predict(random_data, k=5)
+'''
+MNIST Classification
+'''
+# Load Data
+mnist_data = np.load('./sample_data/mnist/mnist_data.npz')
+train_data = mnist_data['train_data']
+test_data = mnist_data['test_data']
+
+# Subset Data If Desired
+test_labels = test_data[:2000, 0]
+test_data = test_data[:2000, 1:].astype(np.float)
+train_labels = train_data[:6000, 0]
+train_data = train_data[:6000, 1:].astype(np.float)
+
+# Create and Train Classifier
+knn = KNNClassification(metric="manhattan")
+knn.train(train_labels, train_data)
+
+# Get Predictions
+predictions = knn.predict(test_data, k=5)
+
+print("Test Labels:\n" + str(test_labels))
+print("Predicted Labels:\n" + str(predictions))
+
+accuracy = sum(test_labels == predictions)/test_labels.size
+print("Accuracy: " + str(accuracy))
+
+
+'''
+Using A Distance Metric From Another Package
+'''
+from scipy.spatial import distance
+
+train_data = np.array([[1., 2., 3.],
+                       [5., 6., 7.],
+                       [8., 9., 10.]])
+train_labels = np.array([0, 1, 1])
+
+test_data = np.array([[5., 10., 15.],
+                      [10., 20., 30.]])
+
+# Needed To Swap Input Order And Set Metric Argument
+def scipy_cityblock(vectors_a, vectors_b):
+    return distance.cdist(vectors_b, vectors_a, 'cityblock')
+
+# Create and Train Classifier
+knn = KNNClassification(metric=scipy_cityblock)
+knn.train(train_labels, train_data)
+
+# Get Predictions
+predictions = knn.predict(test_data, k=1)
+
+print("k=1 Predictions:\n" + str(predictions))
+
+
+'''
+Million Song Dataset(MSD) Regression
+'''
+msd_data = np.load('./sample_data/msd/msd_data.npz')
+
+# Note - astype() Used To Make Arrays Contiguous
+train_data = msd_data['train_data']
+train_response = train_data[:600000, 0]
+train_data = train_data[:600000, 1:].astype(np.float)
+
+test_data = msd_data['test_data']
+test_response = test_data[:500, 0]
+test_data = test_data[:500, 1:].astype(np.float)
+
+knn = KNNRegression(metric="manhattan")
+knn.train(train_response, train_data)
+
+# Get Predictions
+predicted_response = knn.predict(test_data, k=35)
+
+print("Actual Response:\n" + str(test_response[:20]))
+print("Predicted Response:\n" + str(predicted_response[:20]))
+
+error = np.sqrt(np.mean(np.square(test_response-predicted_response)))
+print("Root Mean Square Error: " + str(error))
+
+
+'''
+ Simple Example - Classification Using Ball Tree
+'''
+
+train_data = np.array([[4, -2], [5, 5], [8, 7], [-6, -1], [-1, -3], [-4,-8]]).astype(np.float)
+train_labels = np.array([1, 1, 1, 0, 0, 0])
+
+test_data = np.array([[6, 4], [-8, -4]]).astype(np.float)
+
+knn = KNNClassification(metric="euclidean", use_tree=True, tree_leaf_size=3)
+knn.train(train_labels, train_data)
+predictions = knn.predict(test_data, k=3)
+print("k=3 Predictions:\n" + str(predictions))
+
+
+'''
+MNIST Classification - Ball Tree
+'''
+# Load Data
+mnist_data = np.load('./sample_data/mnist/mnist_data.npz')
+train_data = mnist_data['train_data']
+test_data = mnist_data['test_data']
+
+# Subset Data If Desired
+test_labels = test_data[:1000, 0]
+test_data = test_data[:1000, 1:].astype(np.float)
+train_labels = train_data[:1000, 0]
+train_data = train_data[:1000, 1:].astype(np.float)
+
+# Create and Train Classifier
+knn = KNNClassification(metric="manhattan", use_tree=True, tree_leaf_size=100)
+knn.train(train_labels, train_data)
+
+# Get Predictions
+predictions = knn.predict(test_data, k=3)
+
+print("Test Labels:\n" + str(test_labels))
+print("Predicted Labels:\n" + str(predictions))
+
+accuracy = sum(test_labels == predictions)/test_labels.size
+print("Accuracy: " + str(accuracy))
+
+
+'''
+Million Song Dataset(MSD) Regression - Ball Tree
+'''
+msd_data = np.load('./sample_data/msd/msd_data.npz')
+
+train_data = msd_data['train_data']
+train_response = train_data[:60000, 0]
+train_data = train_data[:60000, 1:].astype(np.float)
+
+test_data = msd_data['test_data']
+test_response = test_data[:500, 0]
+test_data = test_data[:500, 1:].astype(np.float)
+
+knn = KNNRegression(metric="manhattan", use_tree=True, tree_leaf_size=3)
+knn.train(train_response, train_data)
+
+# Get Predictions
+predicted_response = knn.predict(test_data, k=35)
+
+print("Actual Response:\n" + str(test_response[:20]))
+print("Predicted Response:\n" + str(predicted_response[:20]))
+
+error = np.sqrt(np.mean(np.square(test_response-predicted_response)))
+print("Root Mean Square Error: " + str(error))
+
+
+'''
+ Structured Data
+'''
+# Ball Trees Benefit Heavily If Data Is Has Structure (Naturally Partitioned Into Hyper Spheres "Balls")
+
+points_per_region = 200
+number_of_regions = 16
+
+random_data = np.empty((points_per_region * number_of_regions,2))
+region = 0
+
+for i in range(-30, 30, 15):
+    for j in range(-30, 30, 15):
+        mu = np.array([i+7.5, j+7.5])
+        sigma = np.array([[1, 0], [0, 1]])
+        random_data[region:region+points_per_region] = np.random.multivariate_normal(mu, sigma, points_per_region)
+        region += points_per_region
+
+labels = np.repeat(np.arange(number_of_regions), points_per_region)
+
+knn = KNNClassification(use_tree=True, tree_leaf_size=20, metric="euclidean")
+knn.train(labels, random_data)
+# Get Predictions
+predictions = knn.predict(random_data, k=5)