def multiplyData(y,node,edge): y_ = [] node_ = [] edge_ = [] N = len(node) for l, n , e in zip(y,node,edge): samples = sampleSubSequences(n.shape[0],extra_samples,min_length_sequence) for s in samples: if copy_start_state: ll = [0] if s[0] > 0: ll = ll + range(s[0],s[1]) else: ll = range(s[0],s[1]) y_.append(l[ll]) node_.append(n[ll,:]) edge_.append(e[ll,:]) else: y_.append(l[s[0]:s[1]]) node_.append(n[s[0]:s[1],:]) edge_.append(e[s[0]:s[1],:]) N += 1 y = y + y_ edge = edge + edge_ node = node + node_ return N,y,node,edge
def multiplyData(features,sample_ratio): N = 0 for action in actions: new_samples = [] for f in features[action]: N += 1 samples = sampleSubSequences(f.shape[0],int(sample_ratio[action]*extra_samples),min_length_sequence) for s in samples: N += 1 if copy_start_state: ll = [0] if s[0] > 0: ll = ll + range(s[0],s[1]) else: ll = range(s[0],s[1]) new_samples.append(f[ll,:]) else: new_samples.append(f[s[0]:s[1],:]) features[action] = features[action] + new_samples print '{0} {1}'.format(action,len(features[action])) return N,features
def multiplyData(y,node,edge,edge_intra,y_object,node_object,edge_object,edge_intra_object,edge_intra_object_human): y_ = [] y_anticipation_ = [] node_ = [] edge_ = [] edge_intra_ = [] y_object_ = [] y_object_anticipation_ = [] node_object_ = [] edge_object_ = [] edge_intra_object_ = [] edge_intra_object_human_ = [] N = len(node) for l in y: y_anticipation_.append(appendToArray(l[1:],11)) for l, n , e, ei, yo, no, eo, eio, eioh in zip(y,node,edge,edge_intra,y_object,node_object,edge_object,edge_intra_object,edge_intra_object_human): samples = sampleSubSequences(n.shape[0],extra_samples,min_length_sequence) for yo_, no_, eo_, eio_, eioh_ in zip(yo, no, eo, eio, eioh): y_object_.append(yo_) y_object_anticipation_.append(appendToArray(yo_[1:],13)) node_object_.append(no_) edge_object_.append(eo_) edge_intra_object_.append(eio_) edge_intra_object_human_.append(eioh_) for s in samples: if copy_start_state: ll = [0] if s[0] > 0: ll = ll + range(s[0],s[1]) else: ll = range(s[0],s[1]) y_.append(l[ll]) node_.append(n[ll,:]) edge_.append(e[ll,:]) edge_intra_.append(ei[ll,:]) new_list = [(x+1) for x in ll] y_anticipation_.append(appendToArray(l,11,new_list)) for yo_, no_, eo_, eio_, eioh_ in zip(yo, no, eo, eio, eioh): y_object_.append(yo_[ll]) node_object_.append(no_[ll,:]) edge_object_.append(eo_[ll,:]) edge_intra_object_.append(eio_[ll,:]) edge_intra_object_human_.append(eioh_[ll,:]) y_object_anticipation_.append(appendToArray(yo_,13,new_list)) else: y_.append(l[s[0]:s[1]]) node_.append(n[s[0]:s[1],:]) edge_.append(e[s[0]:s[1],:]) edge_intra_.append(ei[s[0]:s[1],:]) new_list = range(s[0]+1,s[1]+1) y_anticipation_.append(appendToArray(l,11,new_list)) for yo_, no_, eo_, eio_, eioh_ in zip(yo, no, eo, eio, eioh): y_object_.append(yo_[s[0]:s[1]]) node_object_.append(no_[s[0]:s[1],:]) edge_object_.append(eo_[s[0]:s[1],:]) edge_intra_object_.append(eio_[s[0]:s[1],:]) edge_intra_object_human_.append(eioh_[s[0]:s[1],:]) y_object_anticipation_.append(appendToArray(yo_,13,new_list)) N += 1 y = y + y_ edge = edge + edge_ node = node + node_ edge_intra = edge_intra + edge_intra_ y_anticipation = y_anticipation_ y_object = y_object_ y_object_anticipation = y_object_anticipation_ edge_object = edge_object_ node_object = node_object_ edge_intra_object = edge_intra_object_ edge_intra_object_human = edge_intra_object_human_ return N,y,node,edge,edge_intra,y_object,node_object,edge_object,edge_intra_object,edge_intra_object_human,y_anticipation,y_object_anticipation
def multiplyData(y, node, edge, edge_intra, y_object, node_object, edge_object, edge_intra_object, edge_intra_object_human): y_ = [] y_anticipation_ = [] node_ = [] edge_ = [] edge_intra_ = [] y_object_ = [] y_object_anticipation_ = [] node_object_ = [] edge_object_ = [] edge_intra_object_ = [] edge_intra_object_human_ = [] N = len(node) for l in y: y_anticipation_.append(appendToArray(l[1:], 11)) for l, n, e, ei, yo, no, eo, eio, eioh in zip(y, node, edge, edge_intra, y_object, node_object, edge_object, edge_intra_object, edge_intra_object_human): samples = sampleSubSequences(n.shape[0], extra_samples, min_length_sequence) for yo_, no_, eo_, eio_, eioh_ in zip(yo, no, eo, eio, eioh): y_object_.append(yo_) y_object_anticipation_.append(appendToArray(yo_[1:], 13)) node_object_.append(no_) edge_object_.append(eo_) edge_intra_object_.append(eio_) edge_intra_object_human_.append(eioh_) for s in samples: if copy_start_state: ll = [0] if s[0] > 0: ll = ll + range(s[0], s[1]) else: ll = range(s[0], s[1]) y_.append(l[ll]) node_.append(n[ll, :]) edge_.append(e[ll, :]) edge_intra_.append(ei[ll, :]) new_list = [(x + 1) for x in ll] y_anticipation_.append(appendToArray(l, 11, new_list)) for yo_, no_, eo_, eio_, eioh_ in zip(yo, no, eo, eio, eioh): y_object_.append(yo_[ll]) node_object_.append(no_[ll, :]) edge_object_.append(eo_[ll, :]) edge_intra_object_.append(eio_[ll, :]) edge_intra_object_human_.append(eioh_[ll, :]) y_object_anticipation_.append( appendToArray(yo_, 13, new_list)) else: y_.append(l[s[0]:s[1]]) node_.append(n[s[0]:s[1], :]) edge_.append(e[s[0]:s[1], :]) edge_intra_.append(ei[s[0]:s[1], :]) new_list = range(s[0] + 1, s[1] + 1) y_anticipation_.append(appendToArray(l, 11, new_list)) for yo_, no_, eo_, eio_, eioh_ in zip(yo, no, eo, eio, eioh): y_object_.append(yo_[s[0]:s[1]]) node_object_.append(no_[s[0]:s[1], :]) edge_object_.append(eo_[s[0]:s[1], :]) edge_intra_object_.append(eio_[s[0]:s[1], :]) edge_intra_object_human_.append(eioh_[s[0]:s[1], :]) y_object_anticipation_.append( appendToArray(yo_, 13, new_list)) N += 1 y = y + y_ edge = edge + edge_ node = node + node_ edge_intra = edge_intra + edge_intra_ y_anticipation = y_anticipation_ y_object = y_object_ y_object_anticipation = y_object_anticipation_ edge_object = edge_object_ node_object = node_object_ edge_intra_object = edge_intra_object_ edge_intra_object_human = edge_intra_object_human_ return N, y, node, edge, edge_intra, y_object, node_object, edge_object, edge_intra_object, edge_intra_object_human, y_anticipation, y_object_anticipation