def init_matlab_toolbox(matlab_cobra_path=None):
"""initialize the matlab cobra toolbox, and load its functions
into mlab's namespace (very useful for ipython tab completion)
matlab_cobra_path: the path to the directory containing the MATLAB
cobra installation. Using the default None will attempt to find the
toolbox in the MATLAB path"""
if matlab_cobra_path is None:
matlab_cobra_path, tmp = os.path.split(
cobra.matlab.which('initCobraToolbox'))
if not os.path.isfile(os.path.join(matlab_cobra_path, "initCobraToolbox.m")):
print "initCobraToolbox not found in given path"
return
# store the current directory so we can return to it
curdir = os.path.abspath(os.curdir)
# if the user has a pathdef file, it will not get used by the MATLAB
# engine, so it needs to be called manually
try:
os.chdir(matlab_home)
matlab.path(matlab.pathdef())
except:
pass
os.chdir(matlab_cobra_path)
matlab.initCobraToolbox()
os.chdir(curdir)
# discover MATLAB functions
with warnings.catch_warnings():
warnings.simplefilter("ignore")
os.path.walk(matlab_cobra_path, _probe, None)
os.path.walk(mlab_path, _probe, None)
for function in _useful_matlab_functions:
exec("matlab.%s.__doc__" % function)
def add_path(path):
from mlabwrap import mlab
path_str = mlab.path(nout=1)
paths = path_str.split(';')
for p in paths:
print p
if not path in paths:
mlab.path(path_str, path)
def testForSource(Ai, Aj, Av, Gcollab_base, feature_graphs, src, beta):
# Get the features from G for the given source node
features = getFeatures.get_walk_features(Gcollab_base, feature_graphs, src)
newAi= Ai
newAj= Aj
# convert nodeorder into a dict for mapping
# mapping[oldID]= newID
# nodeorder[newID-1]= oldID
# newID starts from 1, and not 0
mapping = {}
rmap = {}
cnt = 1
flat_features = []
for tup, feature in features.iteritems():
v = tup[1]
mapping[v] = cnt
rmap[cnt] = v
flat_features.append(map(float, feature))
cnt+=1
for i in range(len(Ai)):
newAi[i]= mapping[Ai[i]]
newAj[i]= mapping[Aj[i]]
newSrcID = mapping[src]
np_Ai, np_Aj, np_Av = interface.sparsePyToMat(Ai, Aj, Av)
np_F = np.array(flat_features)
mlab.path(mlab.path(), "learning/")
#print np_Ai, np_Aj, np_Av, np_F, newSrcID, beta
np_newIDs= mlab.runRW(np_Ai, np_Aj, np_Av, np_F, newSrcID, beta)
newIDs= np_newIDs.tolist()
# convert newIDs to original IDs
origIDs= []
for new_id in newIDs:
origIDs.append(rmap[new_id[0]])
return origIDs
def get_eval_result():
'''
This will invoke matlab-wider-eval script to perform evaluation( will overwrite any previous result)
And load result mat files into here, then return them
'''
import matlab.engine # package install see: https://www.mathworks.com/help/matlab/matlab_external/install-the-matlab-engine-for-python.html
from mlabwrap import mlab # package install see: http://mlabwrap.sourceforge.net/#installation
# make sure 'matlab' executable is in your $PATH
single, _ = make_dirs(mat_bak=True)
current_pwd = os.getcwd()
print "Current location: ", current_pwd
if not DEBUG: mlab.path(mlab.path(), eval_root)
os.chdir(eval_root)
print "Executing matlab, Please wait..."
if not DEBUG: mlab.my_wider_eval('./val')
result_dir = os.path.join(os.getcwd(),
'plot/baselines/Val/setting_int/Ours')
os.chdir(current_pwd)
D = load_result_mat(result_dir, single)
return D, current_pwd
def main(args):
db = args[0]
date1 = args[1]
date2 = args[2]
date3 = args[3]
k = int(args[4])
basename = args[5]
reader = DBReader(db)
print("Getting uid")
uid = reader.uid()
print("Getting all the feature graphs")
feature_graphs = graphutils.get_feat_graphs(db, uid, None, date2)
print("Getting Gcollab_delta graph")
Gcollab_delta = graphutils.get_collab_graph(db, uid, date1, date2)
Gcollab_base = graphutils.get_collab_graph(db, uid, date3, date1)
base_graphs = graphutils.get_base_dict(Gcollab_base, feature_graphs)
graphutils.print_stats(base_graphs)
graphutils.print_graph_stats("Gcollab_delta", Gcollab_delta)
filepath = os.path.join(LEARNING_ROOT, basename + ".mat")
features_matrix_name = "%s_%s" % (basename, FEATURES)
labels_matrix_name = "%s_%s" % (basename, LABELS)
features = consolidateFeatures.consolidate_features_add(
base_graphs, k, Gcollab_delta)
#features = consolidateFeatures.consolidate_features(base_graphs, Gcollab_delta, k)
labels = consolidateFeatures.consolidate_labels(features, Gcollab_delta)
np_train, np_output = interface.matwrapTrain(features, labels)
interface.writeTrain(np_train, np_output, filepath, features_matrix_name,
labels_matrix_name)
# Add learning root to mlab path so that all .m functions are available as mlab attributes
mlab.path(mlab.path(), LEARNING_ROOT)
mlab.training(np_train, np_output)
def main(args):
db = args[0]
date1 = args[1]
date2 = args[2]
date3 = args[3]
k = int(args[4])
basename = args[5]
reader = DBReader(db)
print("Getting uid")
uid = reader.uid()
print("Getting all the feature graphs")
feature_graphs = graphutils.get_feat_graphs(db, uid, None, date2)
print("Getting Gcollab_delta graph")
Gcollab_delta = graphutils.get_collab_graph(db, uid, date1, date2)
Gcollab_base = graphutils.get_collab_graph(db, uid, date3, date1)
base_graphs = graphutils.get_base_dict(Gcollab_base, feature_graphs)
graphutils.print_stats(base_graphs)
graphutils.print_graph_stats("Gcollab_delta", Gcollab_delta)
filepath = os.path.join(LEARNING_ROOT, basename + ".mat")
features_matrix_name = "%s_%s"%(basename, FEATURES)
labels_matrix_name = "%s_%s"%(basename, LABELS)
features = consolidateFeatures.consolidate_features_add(base_graphs, k, Gcollab_delta)
#features = consolidateFeatures.consolidate_features(base_graphs, Gcollab_delta, k)
labels = consolidateFeatures.consolidate_labels(features, Gcollab_delta)
np_train, np_output = interface.matwrapTrain(features, labels)
interface.writeTrain(np_train, np_output, filepath, features_matrix_name, labels_matrix_name)
# Add learning root to mlab path so that all .m functions are available as mlab attributes
mlab.path(mlab.path(), LEARNING_ROOT)
mlab.training(np_train, np_output)
)
diff0 = (pred[:, :, :, 0].flatten() - targets_test[:, 0])
diff1 = (pred[:, :, :, 1].flatten() - targets_test[:, 1])
diff2 = (pred[:, :, :, 2].flatten() - targets_test[:, 2])
err1 = np.mean(diff0 * diff0)
err2 = np.mean(diff1 * diff1)
err3 = np.mean(diff2 * diff2)
error = (err1 + err2 + err3)/3
print "\nMean square error: {}".format(error)
print "\n\nDISPLAYING\n----------"
from mlabwrap import mlab
seunglab = "/home/luke/Documents/masters/code/seunglab"
matlabpath = "/home/luke/neuron-forests/matlab"
mlab.path(mlab.path(), seunglab + "/vis")
mlab.path(mlab.path(), seunglab + "/segmentation")
mlab.path(mlab.path(), matlabpath + "/vis")
# mlab.BrowseComponents('ii', targets_test.reshape(pred.shape).astype(float), pred)
mlab.vis2(im[tuple(idxs_test)].reshape((pred.shape[0], pred.shape[1], pred.shape[2])),
targets_test.reshape(pred.shape).astype(float), pred, 0.85)
# mlab.path(mlab.path(), seunglab + "/analysis")
# mlab.plot_rand_error(pred, targets_test.reshape(pred.shape).astype(float))
import os
from mlabwrap import mlab
mlab.path(mlab.path(), '../matlab')
hog = mlab.get_HOG('../seed_patches/1970393557375786_185_178_112_168.jpg')
print hog
def add_path(path):
from mlabwrap import mlab
path_str = mlab.path(nout=1)
paths = path_str.split(';')
if not path in paths:
mlab.path(path_str, path)
import numpy as np
from mlabwrap import mlab
import pickle
#from motion import *
from python_exp2xyz import exp2xyz
import theano
import scipy.io
import pdb
motion_path = "/afs/cs.stanford.edu/u/barak/Workspace/human-motion-modeling/crbm_matlab/Motion/"
mlab.path(mlab.path(), motion_path)
def preprocess(n1, activities_file):
Motion = mlab.preprocess(n1, activities_file)
batchdata, seqlen, data_mean, data_std, offsets = _preprocess2_matlab(Motion)
#shared_x = theano.shared(np.asarray(batchdata, dtype=theano.config.floatX))
return batchdata, seqlen, data_mean, data_std, offsets
def postprocess(visible, data_std, data_mean, offsets):
newdata = mlab.postprocess(visible, data_std, data_mean, offsets)
return newdata
def get_joi(x_t, joi, data_mean, data_std, offsets):
joi_indices = reduce(lambda x, y: x+y, map(lambda x: range(6*x, 6*(x+1)), joi))
return postprocess(x_t, data_std.reshape((1, -1)), data_mean.reshape((1, -1)), offsets)[:, joi_indices]
def final_frame(z_t, final_frame_lookahead):