def fitKernel(x, y, maxLag, maxLead=0, padding=False, mask=None):
regMat, yMask = kerToReg(x, maxLag, maxLead, padding, mask)
model = RegressionModel.load(regMat, "linear")
#TODO: when Series.selectByIndex is available, it should be used here instead of Series.applyValues
yMasked = y.applyValues(lambda v: v[yMask])
return model.fit(yMasked)
def perform_regression(self, regressor_list):
regressor_list = zscore(regressor_list, axis=1)
regression_results = RegressionModel.load(regressor_list, "linear").fit(self.data)
b = regression_results.select("betas").pack()
rsq = regression_results.select("stats").pack()
return regression_results, b, rsq
def perform_regression(self, regressor_list):
regressor_list = zscore(regressor_list, axis=1)
regression_results = RegressionModel.load(regressor_list,
'linear').fit(self.data)
b = regression_results.select('betas').pack()
rsq = regression_results.select('stats').pack()
return regression_results, b, rsq
"""
Example standalone app for mass-univariate regression
"""
import optparse
from thunder import ThunderContext, RegressionModel
if __name__ == "__main__":
parser = optparse.OptionParser(description="fit a regression model",
usage="%prog datafile modelfile outputdir [options]")
parser.add_option("--regressmode", choices=("mean", "linear", "bilinear"),
default="linear", help="form of regression")
opts, args = parser.parse_args()
try:
datafile = args[0]
modelfile = args[1]
outputdir = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="regress")
data = tsc.loadText(datafile)
result = RegressionModel.load(modelfile, opts.regressmode).fit(data)
outputdir += "-regress"
tsc.export(result.select('stats'), outputdir, "stats", "matlab")
tsc.export(result.select('betas'), outputdir, "betas", "matlab")
"""
import optparse
from thunder import ThunderContext, RegressionModel
if __name__ == "__main__":
parser = optparse.OptionParser(
description="fit a regression model",
usage="%prog datafile modelfile outputdir [options]")
parser.add_option("--regressmode",
choices=("mean", "linear", "bilinear"),
default="linear",
help="form of regression")
opts, args = parser.parse_args()
try:
datafile = args[0]
modelfile = args[1]
outputdir = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="regress")
data = tsc.loadText(datafile)
result = RegressionModel.load(modelfile, opts.regressmode).fit(data)
outputdir += "-regress"
tsc.export(result.select('stats'), outputdir, "stats", "matlab")
tsc.export(result.select('betas'), outputdir, "betas", "matlab")
from thunder import ThunderContext, RegressionModel, PCA
if __name__ == "__main__":
parser = optparse.OptionParser(description="fit a regression model",
usage="%prog datafile modelfile outputdir [options]")
parser.add_option("--regressmode", choices=("mean", "linear", "bilinear"), help="form of regression")
parser.add_option("--k", type=int, default=2)
opts, args = parser.parse_args()
try:
datafile = args[0]
modelfile = args[1]
outputdir = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="regresswithpca")
data = tsc.loadSeries(datafile)
model = RegressionModel.load(modelfile, opts.regressmode) # do regression
betas, stats, resid = model.fit(data)
pca = PCA(opts.k).fit(betas) # do PCA
traj = model.fit(data, pca.comps) # get trajectories
outputdir += "-regress"
tsc.export(pca.comps, outputdir, "comps", "matlab")
tsc.export(pca.latent, outputdir, "latent", "matlab")
tsc.export(pca.scores, outputdir, "scores", "matlab")
tsc.export(traj, outputdir, "traj", "matlab")
if __name__ == "__main__":
parser = optparse.OptionParser(
description="fit a regression model",
usage="%prog datafile modelfile outputdir [options]")
parser.add_option("--regressmode",
choices=("mean", "linear", "bilinear"),
help="form of regression")
parser.add_option("--k", type=int, default=2)
opts, args = parser.parse_args()
try:
datafile = args[0]
modelfile = args[1]
outputdir = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="regresswithpca")
data = tsc.loadSeries(datafile)
model = RegressionModel.load(modelfile, opts.regressmode) # do regression
betas, stats, resid = model.fit(data)
pca = PCA(opts.k).fit(betas) # do PCA
traj = model.fit(data, pca.comps) # get trajectories
outputdir += "-regress"
export(pca.comps, outputdir, "comps", "matlab")
export(pca.latent, outputdir, "latent", "matlab")
export(pca.scores, outputdir, "scores", "matlab")
export(traj, outputdir, "traj", "matlab")
"""
Example standalone app for mass-unvariate regression combined with PCA
"""
import argparse
from thunder import ThunderContext, RegressionModel, PCA, export
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="fit a regression model")
parser.add_argument("datafile", type=str)
parser.add_argument("modelfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("regressmode", choices=("mean", "linear", "bilinear"), help="form of regression")
parser.add_argument("--k", type=int, default=2)
args = parser.parse_args()
tsc = ThunderContext.start(appName="regresswithpca")
data = tsc.loadSeries(args.datafile)
model = RegressionModel.load(args.modelfile, args.regressmode) # do regression
betas, stats, resid = model.fit(data)
pca = PCA(args.k).fit(betas) # do PCA
traj = model.fit(data, pca.comps) # get trajectories
outputdir = args.outputdir + "-regress"
export(pca.comps, outputdir, "comps", "matlab")
export(pca.latent, outputdir, "latent", "matlab")
export(pca.scores, outputdir, "scores", "matlab")
export(traj, outputdir, "traj", "matlab")
"""
Example standalone app for mass-univariate regression
"""
import argparse
from thunder import ThunderContext, RegressionModel, export
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="fit a regression model")
parser.add_argument("datafile", type=str)
parser.add_argument("modelfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("regressmode", choices=("mean", "linear", "bilinear"), help="form of regression")
args = parser.parse_args()
tsc = ThunderContext.start(appName="regress")
data = tsc.loadText(args.datafile, args.preprocess)
result = RegressionModel.load(args.modelfile, args.regressmode).fit(data)
outputdir = args.outputdir + "-regress"
export(result.select('stats'), outputdir, "stats", "matlab")
export(result.select('betas'), outputdir, "betas", "matlab")
"""
import argparse
from thunder import ThunderContext, RegressionModel, PCA, export
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="fit a regression model")
parser.add_argument("datafile", type=str)
parser.add_argument("modelfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("regressmode",
choices=("mean", "linear", "bilinear"),
help="form of regression")
parser.add_argument("--k", type=int, default=2)
args = parser.parse_args()
tsc = ThunderContext.start(appName="regresswithpca")
data = tsc.loadSeries(args.datafile)
model = RegressionModel.load(args.modelfile,
args.regressmode) # do regression
betas, stats, resid = model.fit(data)
pca = PCA(args.k).fit(betas) # do PCA
traj = model.fit(data, pca.comps) # get trajectories
outputdir = args.outputdir + "-regress"
export(pca.comps, outputdir, "comps", "matlab")
export(pca.latent, outputdir, "latent", "matlab")
export(pca.scores, outputdir, "scores", "matlab")
export(traj, outputdir, "traj", "matlab")
