Exemple #1
0
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)
Exemple #2
0
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)
Exemple #3
0
import path
from db.interface import * 
from analysis import graphutils
import utils

db = utils.get_small_db()
reader = DBReader(db)

uid = reader.uid()
rowid = uid.values()[0]
print reader.get_users([rowid, rowid])

Gcollab = graphutils.get_collab_graph(db, uid)
feature_graphs = graphutils.get_feat_graphs(db, uid)

base_graphs = graphutils.get_base_dict(Gcollab, feature_graphs)
graphutils.print_stats(base_graphs)