Added python source files as resource

sources/crossValidationTemplate.py

+#
+# CCRMA MIR workshop 2014 code examples and useful functions
+#
+# Ported to SKLearn Python by Steve Tjoa & Leigh M. Smith
+#
+
+import numpy as np
+from sklearn import cross_validation
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn import preprocessing
+import urllib2  # the lib that handles the url stuff
+
+# Here are examples of how scaling functions would be written, however nowdays SciKit
+# Learn will do it for you with the MinMaxScaler!
+
+def scale(x):
+    """
+    Linearly scale data, to a range of -1 to +1.
+    Return the scaled data, the gradient and offset factors.
+    """
+    if x.shape[0] != 1:                   # Make sure that data is matrix and not a vector
+        dataMax = x.max(0)
+        dataRange = dataMax - x.min(0)         # First, find out the ranges of the data
+        multfactor = 2 / dataRange        # Scaling to be {-1 to +1}.  This is a range of "2" 
+        newMaxval = multfactor * dataMax
+        subfactor = newMaxval - 1      # Center around 0, which means subtract 1
+        data = np.empty(x.shape)
+        for featureIndex in range(x.shape[1]):
+            data[:, featureIndex] = x[:, featureIndex] * multfactor[featureIndex] - subfactor[featureIndex]
+    else:                                 # If data is a vector, just return vector and multiplication = 1, subtraction = 0
+        data = x 
+        multfactor = 1
+        subfactor  = 0 
+    return data, multfactor, subfactor
+
+def rescale(featureVector, mf, sf):
+    """
+    featureVector = the unscaled feature vector
+    mf = the multiplication factor used for linear scaling 
+    sf = the subtraction factor used for linear scaling
+    """
+    featureVector_scaled = np.empty(featureVector.shape)
+    for featureIndex in range(featureVector.shape[1]):
+        # linear scale
+        featureVector_scaled[:, featureIndex] = featureVector[:, featureIndex] * mf[featureIndex] - sf[featureIndex]
+    return featureVector_scaled
+
+def crossValidateKNN(features, labels):
+    """
+    This code is provided as a template for cross-validation of KNN classification.
+    Pass into the variables "features", "labels" your own data. 
+
+    As well, you can replace the code in the "BUILD" and "EVALUATE" sections
+    to be useful with other types of Classifiers.
+    """
+    #
+    # CROSS VALIDATION 
+    # The features array is arranged as rows of instances, columns of features in our training set.
+    numInstances, numFeatures = features.shape
+    numFolds = min(10, numInstances) # how many cross-validation folds do you want - (default=10)
+    # divide test set into 10 random subsets
+    indices = cross_validation.KFold(numInstances, n_folds = numFolds)
+
+    errors = np.empty(numFolds)
+    for foldIndex, (train_index, test_index) in enumerate(indices):
+        # SEGMENT DATA INTO FOLDS
+        print('Fold: %d' % foldIndex) 
+        print("TRAIN: %s" % train_index)
+        print("TEST: %s" % test_index)
+    
+        # SCALE
+        scaler = preprocessing.MinMaxScaler(feature_range = (-1, 1))
+        trainingFeatures = scaler.fit_transform(features.take(train_index, 0))
+        # BUILD NEW MODEL - ADD YOUR MODEL BUILDING CODE HERE...
+        model = KNeighborsClassifier(n_neighbors = 3)
+        model.fit(trainingFeatures, labels.take(train_index, 0))
+        # RESCALE TEST DATA TO TRAINING SCALE SPACE
+        testingFeatures = scaler.transform(features.take(test_index, 0))
+        # EVALUATE WITH TEST DATA - ADD YOUR MODEL EVALUATION CODE HERE
+        model_output = model.predict(testingFeatures)
+        print("KNN prediction %s" % model_output) # Debugging.
+        # CONVERT labels(test,:) LABELS TO SAME FORMAT TO COMPUTE ERROR 
+        labels_test = labels.take(test_index, 0)
+        # COUNT ERRORS. matches is a boolean array, taking the mean does the right thing.
+        matches = model_output != labels_test
+        errors[foldIndex] = matches.mean()
+    print('cross validation error: %f' % errors.mean())
+    print('cross validation accuracy: %f' % (1.0 - errors.mean()))
+
+
+def processCorpus(corpusURL):
+    """Read a list of files to process from the text file at corpusURL. Return a list of URLs""" 
+    # Open and read each line
+    urlListTextData = urllib2.urlopen(corpusURL) # it's a file like object and works just like a file
+    for fileURL in urlListTextData: # files are iterable
+        yield fileURL.rstrip()
+    # return [fileURL.rstrip() for fileURL in urlListTextData]
+
+# audioFileURLs = processCorpus("https://ccrma.stanford.edu/workshops/mir2014/SmallCorpus.txt")
+# for audioFileURL in processCorpus("https://ccrma.stanford.edu/workshops/mir2014/SmallCorpus.txt"):
+# print audioFileURL