Init commit

Author: fabeschan

README.md

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+Required python 2.7 modules:
+    - IPython
+    - numpy
+    - matplotlib
+    - sklearn
+    - skimage
+
+HOW TO RUN:
+-----------
+
+Run cmm.py
+>> python cmm.py
+
+
+Configuring: (scroll towards the end of cmm.py)
+Edit pieces in cmm.py to select source pieces:
+    pieces = ["mid/hilarity.mid", "mid/froglegs.mid", "mid/easywinners.mid"]
+
+Toggle between two modes: mixture or segmentation (I haven't had a chance to figure out a way to combine the two):
+    segmentation = True
+    all_keys = False
+
+
+NOTES:
+------
+
+- The cached folder holds cached computations. Delete them if they are costing you problems. Note that recalculating stuff will take a while.
+
+- All midi files in mid are properly quantized (i.e. each note type has a regular number of ticks). The music generation system requires that additional midi files must also be properly quantized to function properly.
+

analyze.py

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+'''
+analyze.py
+
+This file sets up infrastructure for the similarity measure using a classifier
+that compares two music segments and gives a score between 0 and 1
+
+'''
+
+from sklearn import datasets, neighbors, linear_model, svm
+from sklearn.metrics import confusion_matrix
+import numpy as np
+import random
+from similar_sections import ss
+import sys
+
+class train_set(object):
+    def  __init__(self, data, target):
+        self.data = data
+        self.target = target
+
+def generate():
+    gen = ss.generate_targets_subset()
+    random.shuffle(gen)
+    target = np.array([ v[2] for v in gen ])
+    data = np.array([ f[0].compare_with(f[1]) for f in gen ])
+    return train_set(data, target)
+
+def train_classifier(sdata, classifier=None):
+    digits = sdata
+
+    X_digits = digits.data
+    y_digits = digits.target
+
+    n_samples = len(X_digits)
+
+    # data
+    X_train = X_digits[:]
+    y_train = y_digits[:]
+
+    if not classifier:
+        #classifier = svm.NuSVC(nu=0.01, probability=True)
+        #classifier = linear_model.RidgeClassifierCV()
+        classifier = linear_model.LogisticRegression(C=3.0)
+
+    classifier_fit = classifier.fit(X_train, y_train)
+    return classifier_fit
+
+
+def test(sdata, classifier=None, verbose=True, verboseverbose=False):
+    digits = sdata
+
+    X_digits = digits.data
+    y_digits = digits.target
+
+    n_samples = len(X_digits)
+
+    # data
+    X_train = X_digits[:.85 * n_samples]
+    y_train = y_digits[:.85 * n_samples]
+
+    # truths/target
+    X_test = X_digits[.85 * n_samples:]
+    y_test = y_digits[.85 * n_samples:]
+
+    if not classifier:
+        classifier = linear_model.RidgeClassifierCV()
+
+    classifier_fit = classifier.fit(X_train, y_train)
+
+    pred = classifier_fit.predict(X_test)
+    score = classifier_fit.score(X_test, y_test)
+
+    if verboseverbose:
+        # print the matrix of feature scores
+        big_matrix = np.array([ np.hstack((X_test[i], y_test[i])) for i in xrange(len(X_test)) ])
+        print ['Tr0Rhyt','Tr0TopL','Tr1Rhyt','Tr1TopL','Truth']
+        print big_matrix
+    if verbose:
+        print 'TRUTH:', y_test
+        print 'PREDN:', pred
+        print ('Classifier score: %f' % score)
+
+    return score, pred, y_test
+
+def evaluate_n(n, sdata, classifier):
+    avg_score = 0.0
+    pred_overall, y_test_overall = np.array([]), np.array([])
+    for i in xrange(n):
+        score, pred, y_test = test(sdata, classifier, verbose=False if n > 1 else True)
+        avg_score += score / n
+        pred_overall = np.hstack((pred_overall, pred))
+        y_test_overall = np.hstack((y_test_overall, y_test))
+
+        sys.stdout.write("\r(Progress: %d/%d)" % (i, n))
+        sys.stdout.flush()
+    else:
+        sys.stdout.write("\r")
+        sys.stdout.flush()
+
+    print "---- Num of Repetitions:", n
+    print "---- Average Score:", avg_score
+    np.set_printoptions(linewidth=999999)
+    print confusion_matrix(y_test_overall, pred_overall)
+
+if __name__ == '__main__':
+
+    # three classifiers to choose from omgz
+    svm = svm.NuSVC(nu=0.02)
+    ridge = linear_model.RidgeClassifierCV()
+    knn = neighbors.KNeighborsClassifier()
+    lr = linear_model.LogisticRegression(C=10.0)
+
+    n = 40
+
+    if len(sys.argv) == 2:
+        n = int(sys.argv[1])
+
+    evaluate_n(n, generate(), lr)