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jblitzar
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github-python-corpus

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return breakPoints, plotPoints
#Run cross-validation up to Kmax for a set of lambdas
#Return: train and test set likelihood for every K, lambda
def GGSCrossVal(data, Kmax=25, lambList = [0.1, 1, 10], features = [], verbose = False):
data = data.T
if (features == []):
features = range(data.shape[1])
data = data[:,features]
origSize, n = data.shape
np.random.seed(0)
ordering = range(origSize)
random.shuffle(ordering)
trainTestResults = []
#For each lambda, run the 10 folds in parallel
numProcesses = min(multiprocessing.cpu_count(),10 )
pool = multiprocessing.Pool(processes = numProcesses)
for lamb in lambList:
mseList = []
trainList = []
returnList = pool.map(multi_run_wrapper, [(0,data, Kmax, lamb, verbose, origSize, n, ordering),
(1,data, Kmax, lamb, verbose, origSize, n, ordering),
(2,data, Kmax, lamb, verbose, origSize, n, ordering),
(3,data, Kmax, lamb, verbose, origSize, n, ordering),
(4,data, Kmax, lamb, verbose, origSize, n, ordering),
(5,data, Kmax, lamb, verbose, origSize, n, ordering),
(6,data, Kmax, lamb, verbose, origSize, n, ordering),
(7,data, Kmax, lamb, verbose, origSize, n, ordering),
(8,data, Kmax, lamb, verbose, origSize, n, ordering),
(9,data, Kmax, lamb, verbose, origSize, n, ordering)])
#Accumulate results
for i in range(10):
for j in returnList[i][0]:
mseList.append(j)
for j in returnList[i][1]:
trainList.append(j)
#Get average of the 10 folds
plotVals = map(list, zip(*mseList))
maxBreaks = max(plotVals[0])+1
testAvg = []
for i in range(maxBreaks):
num = 0
runsum = 0
for j in range(len(plotVals[0])):
if (plotVals[0][j] == i):
runsum = runsum + plotVals[1][j]
num = num + 1
testAvg.append(float(runsum)/num)
plotVals2 = map(list, zip(*trainList))
trainAvg = []
for i in range(maxBreaks):
num = 0
runsum = 0
for j in range(len(plotVals2[0])):
if (plotVals[0][j] == i):
runsum = runsum + plotVals2[1][j]
num = num + 1
trainAvg.append(float(runsum)/num)
#Combine results for all lambdas into one list and return that
trainTestResults.append((lamb, (trainAvg, testAvg)))
return trainTestResults
#Find and return the means/regularized covariance of each segment for a given set of breakpoints
def GGSMeanCov(data, breakpoints, 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
numSegments = len(breakpoints) - 1
mean_covs = []
for i in range(numSegments):
#Get mean and regularized covariance of current segment
tempData = data[breakpoints[i]:breakpoints[i+1],:]
m,n = tempData.shape
empMean = np.mean(tempData, axis=0)
empCov = np.cov(tempData.T,bias = True)
regularizedCov = empCov + float(lamb)*np.identity(n)/m
mean_covs.append((empMean, regularizedCov))
return mean_covs