#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy, scipy, matplotlib.pyplot as plt, sklearn, librosa, urllib, IPython.display, stanford_mir
plt.rcParams['figure.figsize'] = (14,5)


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# # K-Nearest Neighbor

# We can appreciate why we need additional intelligence in our systems -- heuristics don't go very far in the world of complex audio signals.  We'll be using scikit-learn's implementation of the k-NN algorithm for our work here.  It proves be a straightforward and easy-to-use implementation.  The steps and skills of working with one classifier will scale nicely to working with other, more complex classifiers.

# ## Training Data

# Let's begin by loading some training data. We will use the following shortcut:

# In[2]:


training_features, training_labels, scaler = stanford_mir.get_features(collection="drum_samples_train")


# Show the training labels. `0` is a kick drum, and `1` is a snare drum.

# In[3]:


print training_labels


# Plot the training data:

# In[4]:


plt.scatter(training_features[:10,0], training_features[:10,1])
plt.scatter(training_features[10:,0], training_features[10:,1], color='r')
plt.xlabel('Zero Crossing Rate')
plt.ylabel('Spectral Centroid')


# ## Test Data

# Compute features from a test data set of 30 kick drum samples and 30 snare drum samples. We will re-use the `MinMaxScaler` used during training.

# In[6]:


test_features, test_labels, _ = stanford_mir.get_features(collection="drum_samples_test", scaler=scaler)


# Show the test labels:

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print test_labels


# Plot the test feature vectors. Note that this uses the same scaling function used during training. Therefore, some test feature vectors may exceed the range [-1, 1].

# In[18]:


plt.scatter(test_features[test_labels==0,0], test_features[test_labels==0,1])
plt.scatter(test_features[test_labels==1,0], test_features[test_labels==1,1], color='r')
plt.xlabel('Zero Crossing Rate')
plt.ylabel('Spectral Centroid')


# ## Building the K-NN Model

# Build a k-NN model for the snare drums using scikit.learn's [KNeighborsClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) class.

# In[19]:


model = sklearn.neighbors.KNeighborsClassifier(n_neighbors=1)


# To train a scikit-learn classifier, use the classifier object's `fit` method:

# In[20]:


model.fit(training_features, training_labels)


# To test the classifier on a set of (test) feature vectors, use the `predict` method:

# In[21]:


model.predict(test_features)


# Evaluate the model accuracy on the test data.

# In[22]:


model.score(test_features, test_labels)


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