text
stringlengths 1
93.6k
|
|---|
args = parser.parse_args()
|
if torch.cuda.is_available():
|
device = torch.device('cuda')
|
elif torch.backends.mps.is_available():
|
device = torch.device('mps')
|
else:
|
device = torch.device('cpu')
|
whole_song_expr = WholeSongGeneration.init_pipeline(
|
frm_model_folder=args.mpath0,
|
ctp_model_folder=args.mpath1,
|
lsh_model_folder=args.mpath2,
|
acc_model_folder=args.mpath3,
|
frm_model_id=args.mid0,
|
ctp_model_id=args.mid1,
|
lsh_model_id=args.mid2,
|
acc_model_id=args.mid3,
|
debug_mode=args.debug,
|
device=None
|
)
|
whole_song_expr.main(
|
n_sample=args.nsample,
|
nbpm=4,
|
nspb=4,
|
phrase_string=args.pstring,
|
key=args.key,
|
is_major=args.minor,
|
demo_dir=args.demo_dir
|
)
|
# <FILESEP>
|
import numpy as np
|
import numpy.linalg as alg
|
import scipy as spy
|
import matplotlib.pyplot as plt
|
import time
|
from itertools import *
|
import sys
|
import math
|
import random
|
import datetime as DT
|
from matplotlib.dates import date2num
|
import multiprocessing
|
from sys import platform as _platform
|
#Find K breakpoints on the data at a specific lambda
|
#Returns: The K breakpoints, along with all intermediate breakpoints (for k < K) and their corresponding
|
# covariance-regularized maximum likelihoods
|
def GGS(data, Kmax, lamb, features = [], verbose = False):
|
data = data.T
|
#Select the desired features
|
if (features == []):
|
features = range(data.shape[1])
|
data = data[:,features]
|
m,n = data.shape
|
#Initialize breakpoints
|
breaks = [0,m+1]
|
breakPoints = [breaks[:]]
|
plotPoints = [calculateLikelihood(data, breaks,lamb)]
|
#Start GGS Algorithm
|
for z in range(Kmax):
|
numBreaks = z+1
|
newInd = -1
|
newVal = +1
|
#For each segment, find breakpoint and increase in LL
|
for i in range(numBreaks):
|
tempData = data[breaks[i]:breaks[i+1], :]
|
ind, val = addBreak(tempData, lamb)
|
if(val < newVal):
|
newInd = ind + breaks[i]
|
newVal = val
|
#Check if our algorithm is finished
|
if(newVal == 0):
|
print "We are done adding breakpoints!"
|
print breaks
|
return breaks, plotPoints
|
#Add new breakpoint
|
breaks.append(newInd)
|
breaks.sort()
|
if (verbose == True):
|
print "Breakpoint occurs at sample number: ", newInd, ", LL = ", newVal
|
print len(breaks) - 2, breaks
|
#Adjust current locations of the breakpoints
|
breaks = adjustBreaks(data,breaks,[newInd],lamb,verbose)[:]
|
#Calculate likelihood
|
ll = calculateLikelihood(data,breaks,lamb)
|
breakPoints.append(breaks[:])
|
plotPoints.append(ll)
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.