def load_engine(sdf_files, feature_matrix, dimension):
"""
Function that converts the given sdf_files into instances of the sdf_class, then loads them into nearpy Engine.
Parameters
sdf_files: a list of sdf_files with their pathname from the current directory. Intended to be fed in from `find_sdf(root_dir)`
feature_matrix: matrix of training data features to be loaded into engine
dimension: dimensionality of the feature vectors used for LSH (here: number of cluster centers)
Returns
engine: instance of a nearpy engine with all of sdf_files loaded
Sample Usage
>>> engine = load_engine(sdf_files)
"""
#dimension here can be altered as well
rbp = RandomBinaryProjections('rbp',10)
engine = Engine(dimension, lshashes=[rbp])
count = 0
for index,file_ in enumerate(sdf_files):
#print file_
if count % 100 == 0:
print 'Converted %d files' %(count)
converted = SDF(file_)
converted.set_feature_vector(feature_matrix[index])
converted.add_to_nearpy_engine(engine)
count += 1
return engine
def load_engine(sdf_files, feature_matrix, dimension):
"""
Function that converts the given sdf_files into instances of the sdf_class, then loads them into nearpy Engine.
Parameters
sdf_files: a list of sdf_files with their pathname from the current directory. Intended to be fed in from `find_sdf(root_dir)`
feature_matrix: matrix of training data features to be loaded into engine
dimension: dimensionality of the feature vectors used for LSH (here: number of cluster centers)
Returns
engine: instance of a nearpy engine with all of sdf_files loaded
Sample Usage
>>> engine = load_engine(sdf_files)
"""
#dimension here can be altered as well
rbp = RandomBinaryProjections('rbp', 10)
engine = Engine(dimension, lshashes=[rbp])
count = 0
for index, file_ in enumerate(sdf_files):
#print file_
if count % 100 == 0:
print 'Converted %d files' % (count)
converted = SDF(file_)
converted.set_feature_vector(feature_matrix[index])
converted.add_to_nearpy_engine(engine)
count += 1
return engine
