def get_madelon_data(): data_dir = os.sep.join(__file__.split(os.sep)[:-3] + ["data"]) filename_dat = data_dir + os.sep + "madelon_train.data" filename_lab = data_dir + os.sep + "madelon_train.labels" data = loadtxt(filename_dat) lab = loadtxt(filename_lab) return data, lab
def signal(path, skiprows=1): """ Loads .ibw or ASCII files and return it as a numpy.ndarray. Parameters ---------- path : string Path to signal file. Returns ------- signal_array : (n_points, n_signals) array_like 2D real-valued signal array loaded from given .ibw file. """ # Get the path and check what the extension is. ext = splitext(path)[1] if ext.lower() == '.ibw': signal_array = loadibw(path)['wave']['wData'] # Load data. elif ext.lower() == '.txt': signal_array = loadtxt(path, skiprows=skiprows) else: print "Unrecognized file type!" sys.exit(0) signal_array.flags.writeable = True # Make array writable. return signal_array
def get_points(filename): try: points = loadtxt(filename) except: print "loadtxt failed--reading points manually" with open(filename, 'r') as f: lines = [line.strip(' \n').split(' ') for line in f.readlines()] points = [] for line in lines: points.append([float(num) for num in line]) points = np.array(points) return points
def get_glass_data(): data_dir = os.sep.join(__file__.split(os.sep)[:-3] + ["data"]) filename = data_dir + os.sep + "glass.data" data = loadtxt(filename, delimiter=",") # create a binary "window glass" vs "non-window glass" labelling lab = data[:, -1] lab = array([1. if x <= 4 else -1.0 for x in lab]) # cut off ids and labeling data = data[:, 1:-1] return data, lab
def get_pima_data(): data_dir = os.sep.join(__file__.split(os.sep)[:-3] + ["data"]) filename = data_dir + os.sep + "pima-indians-diabetes.data" data = loadtxt(filename, delimiter=",") # create labelling lab = data[:, -1] lab = array([1. if x == 1 else -1.0 for x in lab]) # cut off labeling data = data[:, :-1] return data, lab
def assert_file_matrix(self, filename, M): try: with open(filename): m = loadtxt(filename) # python loads vectors as 1d-arrays, but we want 2d-col-vectors if len(shape(m)) == 1: m = reshape(m, (len(m), 1)) self.assertEqual(M.shape, m.shape) self.assertLessEqual(norm(m - M), 1e-5) return True except IOError: return False
def __init__ (self, worldfile): with open (worldfile) as world: reader = csv.reader (world) names = {} for k, line in enumerate (reader): if not "#" in line [0]: names [line [0]] = k data = loadtxt (worldfile, delimiter = ',', usecols = range (1, 7), dtype = float64) self.names = names self.radii = data [:, 0] self.masses = data [:, 1] self.positions = data [:, 2:4] self.velocities = data [:, 4:] self.accelerations = zeros ( (len (self.names),) + self.positions.shape, dtype = float64) self.mm = outer (self.masses, self.masses) self.diagind = tuple (range (0, len (self.accelerations))) self.count = len (self.names) self.time = 0
def signal(path, skiprows=0): """ Loads .ibw or ASCII files and return it as a numpy.ndarray. :param path: Path to signal file. :type path: string :param skiprows: :type skiprows: int, optional :returns: 2D real-valued signal array loaded from given .ibw file. :rtype: (n_points, n_signals) array_like """ # Get the path and check what the extension is. ext = splitext(path)[1] if ext.lower() == '.ibw': signal_array = loadibw(path)['wave']['wData'] # Load data. elif ext.lower() == '.txt': signal_array = loadtxt(path, skiprows=skiprows) else: print("Unrecognized file type!") sys.exit(0) try: signal_array.flags.writeable = True # Make array writable. except: pass return signal_array
from numpy.oldnumeric.random_array import permutation from matplotlib.pyplot import title, plot, figure, show, draw, clf, contour,\ xlim, ylim, imshow from numpy.ma.core import arange, mean, reshape, shape, sqrt, floor, zeros from kameleon_mcmc.kernel.PolynomialKernel import PolynomialKernel from numpy.linalg.linalg import norm from numpy.lib.npyio import loadtxt #from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import KameleonWindowLearnScale plotting = False pkernel = PolynomialKernel(degree=3) samples_long = loadtxt( "/nfs/home2/dino/kamh-results/StandardMetropolis_PseudoMarginalHyperparameterDistribution_merged_samples.txt" ) samples_long = samples_long[:10000] # f_long=open("/nfs/home2/dino/kamh-results/long_experiment_output.bin") # experiment_long=load(f_long) # f_long.close() # thin_long=100 # mcmc_chain_long=experiment_long.mcmc_chain # burnin=mcmc_chain_long.mcmc_params.burnin # indices_long = range(burnin, mcmc_chain_long.iteration,thin_long) # samples_long=mcmc_chain_long.samples[indices_long] mu_long = mean(samples_long, 0) print 'using this many samples for the long chain: ', shape(samples_long)[0] how_many_chains = 20