def show5HexagramsForFileList(annotationFileList):
cep = ll.ChromaEvaluationParameters(stepSize=2048, smoothingTime=1.2)
chromaEvaluator = ll.AnnotatedChromaEvaluator(cep)
chromas = chromaEvaluator.loadChromasForAnnotationFileList(annotationFileList)
fig, ax = plt.subplots(nrows=1,ncols=5,figsize=(12,2.6), dpi= 90, facecolor='w', edgecolor='k')
for axx in ax:
axx.axes.get_xaxis().set_visible(False)
axx.axes.get_yaxis().set_visible(False)
ax[0].set_title("Maj")
if (any(chromas.kinds == 'maj') > 0):
maj = preprocessing.normalize(chromas.chromas[chromas.kinds == 'maj'], norm='l1')
#sns.violinplot(data=pd.DataFrame(maj, columns=degrees), inner="point", ax=ax[0][0])
plots.plotMajHexagram(ax[0], maj)
ax[1].set_title("Min")
if (any(chromas.kinds == 'min') > 0):
min = preprocessing.normalize(chromas.chromas[chromas.kinds == 'min'], norm='l1')
#sns.violinplot(data=pd.DataFrame(min, columns=degrees), inner="point", ax=ax[0][1])
plots.plotMinHexagram(ax[1], min)
ax[2].set_title("Dom")
if (any(chromas.kinds == 'dom') > 0):
dom = preprocessing.normalize(chromas.chromas[chromas.kinds == 'dom'], norm='l1')
#sns.violinplot(data=pd.DataFrame(dom, columns=degrees), inner="point", ax=ax[0][2])
plots.plotDomHexagram(ax[2], dom)
ax[3].set_title("Hdim7")
if (any(chromas.kinds == 'hdim7') > 0):
hdim7 = preprocessing.normalize(chromas.chromas[chromas.kinds == 'hdim7'], norm='l1')
#sns.violinplot(data=pd.DataFrame(hdim7, columns=degrees), inner="point", ax=ax[0][3])
plots.plotHdim7Hexagram(ax[3], hdim7)
ax[4].set_title("Dim")
if (any(chromas.kinds == 'dim') > 0):
dim = preprocessing.normalize(chromas.chromas[chromas.kinds == 'dim'], norm='l1')
#sns.violinplot(data=pd.DataFrame(dim, columns=degrees), inner="point", ax=ax[0][4])
plots.plotDimHexagram(ax[4], dim)
plt.tight_layout()
plt.show()
def trainBasicChordGMM(chromaEvaluationParameters, basicGMMParameters,
jsonListFile):
chromaEvaluator = ll.AnnotatedChromaEvaluator(chromaEvaluationParameters)
gmms = list(
map(
lambda x: GaussianMixture(
n_components=basicGMMParameters.nComponents[x],
covariance_type=basicGMMParameters.covarianceTypes[x],
max_iter=200,
random_state=8), range(N_CHORD_KINDS)))
gmm = BasicChordGMM(basicGMMParameters, gmms)
gmm.fit(chromaEvaluator.loadChromasForAnnotationFileListFile(jsonListFile))
return gmm
def findOptimalNComponentsByBIC(trainingChromaParams, f = 1, min_components=1, max_components=30):
# gmm = chordModel.trainBasicChordGMM(trainingChromaParams, gmmParams, TRAIN_FILES_LIST_PATTERN.format(f))
#gmm.saveModel(modelPattern.format(f))
fig, ax = plt.subplots(nrows=3,ncols=2)
ax = np.reshape(np.array(ax), (1, np.size(ax)))[0]
res = np.zeros(chordModel.N_CHORD_KINDS)
segments = ll.AnnotatedChromaEvaluator(trainingChromaParams). \
loadChromasForAnnotationFileListFile(TRAIN_FILES_LIST_PATTERN.format(f))
for i in range(chordModel.N_CHORD_KINDS):
chordname = chordModel.CHORD_KINDS[i]
samples = segments.chromas[segments.kinds ==chordname]
bics = estimateBICs(samples,\
min_components=min_components,\
max_components=min(max_components, len(samples)))
ax[i].set_title(chordname)
ax[i].plot(bics)
minIndex = np.argmin(bics)
print(chordname, "BIC: ", bics[minIndex], " N: ", minIndex + 1)
res[i] = minIndex + 1
plt.show()
return res
def trainCV(trainingChromaParams):
chromaEvaluator = ll.AnnotatedChromaEvaluator(trainingChromaParams)
for f in FOLD_NUMBERS:
chromas = chromaEvaluator.loadChromasForAnnotationFileListFile(TRAIN_FILES_LIST_PATTERN.format(f))
betas = np.empty(N_CHORD_KINDS, dtype='object')
partitions = np.empty(N_CHORD_KINDS, dtype='object')
for k in xrange(N_CHORD_KINDS):
chord = preprocessing.normalize(
chordModel.substituteZeros(chromas.chromas[chromas.kinds == CHORD_KINDS[k]]), norm='l1')
sortedDegrees = plots.sortedDegrees(chord, method='mean')
#partition = [degrees.index(x) for x in sortedDegrees[0:len(sortedDegrees) / 2]]
partition = [degrees.index(x) for x in sortedDegrees[0:7]]
partitions[k] = partition
a = plots.estimatePartition(partition, chord)
params = beta.fit(a, floc=0, fscale=1)
plt.hist(a, 60, normed=True)
rv = beta(*params)
betas[k] = rv
#x = np.linspace(0, 1)
#plt.plot(x, rv.pdf(x), lw=2)
#plt.plot(x, rv.cdf(x), lw=2)
#plt.show()
model = Model2(partitions, betas)
model.saveModel(MODEL_PATTERN.format(f))
import plots
import lowLevelFeatures as ll
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import random
from sklearn import preprocessing
import matplotlib.pyplot as plt
from plots import degrees
import math
import chordModel
import numpy as np
cep = ll.ChromaEvaluationParameters(stepSize=2048, smoothingTime=1.2)
chromaEvaluator = ll.AnnotatedChromaEvaluator(cep)
chromas = chromaEvaluator.loadChromasForAnnotationFileListFile('ready.txt')
dMaj = pd.DataFrame(data=preprocessing.normalize(chromas.chromas[chromas.kinds == 'maj'], norm='l1'), columns=degrees)
#g = sns.jointplot('VI', 'VII', dMaj, kind="kde")
maj = preprocessing.normalize(chromas.chromas[chromas.kinds == 'maj'], norm='l1')
fig, ax = plt.subplots()
plots.plotMajHexagram(ax, maj, 60)
# partition
partition = [degrees.index('I'), degrees.index('III'), degrees.index('V')]
a = plots.estimatePartition(partition, maj)
sns.distplot(a)
plt.show()
partition = [degrees.index('I'), degrees.index('II'), degrees.index('III'), degrees.index('IV'), degrees.index('V'), degrees.index('VI'), degrees.index('VII')]
a = plots.estimatePartition(partition, maj)
sns.distplot(a)