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proj.py
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proj.py
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#!/usr/bin/python
#
# proj.py
#
# This program is distributed under the of the GNU Lesser Public License.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>
import numpy as np;
import matplotlib.pyplot as plt;
from sys import stdout;
from audio import *;
from notes import *;
from testsets import *;
from bayes import *;
framesize = 44100/8;
# Read all data
datasets = [
readNotes(range(0,25), framesize),
readMajors(range(0,25), framesize),
readOctaves(range(0,13), framesize),
readOctMajors(range(0,13), framesize),
readMajorMajors(range(0,13), framesize)
];
# Construct Naive Bayes classifiers for all datasets
nbs = [NaiveBayes() for i in range(6)];
# The single-dataset NBs:
for i in range(5):
nbs[i].addLabelledData(datasets[i]);
nbs[i].learn();
# The 'all' NB:
for data in datasets:
nbs[5].addLabelledData(datasets[i]);
nbs[5].learn();
# Titles for all datasets:
titles = [
"Single Notes",
"Majors",
"Octaves",
"Octave-Majors",
"Double Majors",
"All"
];
# Print a lovely Latex table of results.
table = "";
for i in range(len(nbs)):
table += titles[i];
learn = nbs[i].learningAccuracy();
table += " & {0:.3}\\%".format(100.0*learn[0]/learn[1]);
table += " & {0:.3}\\%".format(100.0*learn[2]/learn[3]);
test = nbs[i].testingAccuracy();
table += " & {0:.3}\\%".format(100.0*test[0]/test[1]);
table += " & {0:.3}\\%".format(100.0*test[2]/test[3]);
table += " \\\\ \n\\hline\n";
print(table);