def SdA_test(data_file, skip_col, network_file, net_structure):
activity_file = data_file.replace(
'.pcl', '_activity') + '_with_' + network_file.split('/')[-1]
raw_activity_file = data_file.replace(
'.pcl', '_rawActivity') + '_with_' + network_file.split('/')[-1]
datasets = PCLfile(data_file, skip_col)
input_data = datasets.get_sample()
input_data = numpy.matrix(input_data)
sample_id = datasets.sample_list
network_fh = open(network_file, 'r')
input_size = input_data.shape[1] #input_size is the number of genes
layer_para = []
for each_layer in xrange(len(net_structure)):
network_fh.next() # skip the layer count line
network_fh.next() # skip 'weight matrix' line
#Get the weight matrix
W = []
input_count = 0
for line in network_fh:
line = line.strip().split('\t')
W.append(line)
input_count += 1
if input_count == input_size: #when it reach the number of genes
break
W = numpy.matrix(W, dtype=float)
network_fh.next() # skip 'hidden bias vector' line
#Get the bias vector of hidden layer
h_bias = []
output_count = 0
for line in network_fh:
line = line.strip().split('\t')
h_bias.append(line)
output_count += 1
if output_count == int(
net_structure[each_layer]
): #when it reach the number of nodes in hidden layer
break
h_bias = numpy.matrix(h_bias, dtype=float)
network_fh.next() # skip 'visible bias vector' line
#Get the bias vector of visible output layer
v_bias = []
input_count = 0
for line in network_fh:
v_bias.append(line)
input_count += 1
if input_count == input_size:
break
v_bias = numpy.matrix(v_bias, dtype=float)
layer_para.append(
(W, h_bias)
) #Weight matrix and hidden bias vector are enough for calculating activities
input_size = net_structure[each_layer]
activity_fh = open(activity_file, 'w')
raw_activity_fh = open(raw_activity_file, 'w')
for each_layer in xrange(len(net_structure)):
activity_fh.write('layer %i \n' % (each_layer + 1))
raw_activity_fh.write('layer %i \n' % (each_layer + 1))
temp = input_data * layer_para[each_layer][
0] #calculate raw activity, before sigmoid transformation
output = logit(temp + layer_para[each_layer][1].T
) #calculate activity, after sigmoid transformation
for each_sample in xrange(output.shape[0]):
activity_fh.write(sample_id[each_sample] + '\t')
raw_activity_fh.write(sample_id[each_sample] + '\t')
numpy.savetxt(activity_fh,
output[each_sample, ],
fmt='%.8f',
delimiter='\t')
numpy.savetxt(raw_activity_fh,
temp[each_sample, ],
fmt='%.8f',
delimiter='\t')
input_data = output
def SdA_test(data_file, skip_col, network_file, net_structure):
activity_file = data_file.replace('.pcl','_activity') + '_with_' + network_file.split('/')[-1]
raw_activity_file = data_file.replace('.pcl','_rawActivity') + '_with_' + network_file.split('/')[-1]
datasets = PCLfile(data_file, skip_col)
input_data = datasets.get_sample()
input_data = numpy.matrix(input_data)
sample_id = datasets.sample_list
network_fh = open(network_file,'r')
input_size = input_data.shape[1] #input_size is the number of genes
layer_para = []
for each_layer in xrange(len(net_structure)):
network_fh.next() # skip the layer count line
network_fh.next() # skip 'weight matrix' line
#Get the weight matrix
W = []
input_count = 0
for line in network_fh:
line = line.strip().split('\t')
W.append(line)
input_count += 1
if input_count == input_size: #when it reach the number of genes
break
W = numpy.matrix(W, dtype= float)
network_fh.next() # skip 'hidden bias vector' line
#Get the bias vector of hidden layer
h_bias = []
output_count = 0
for line in network_fh:
line = line.strip().split('\t')
h_bias.append(line)
output_count += 1
if output_count == int(net_structure[each_layer]): #when it reach the number of nodes in hidden layer
break
h_bias = numpy.matrix(h_bias, dtype = float)
network_fh.next() # skip 'visible bias vector' line
#Get the bias vector of visible output layer
v_bias = []
input_count = 0
for line in network_fh:
v_bias.append(line)
input_count += 1
if input_count == input_size:
break
v_bias = numpy.matrix(v_bias, dtype = float)
layer_para.append((W,h_bias)) #Weight matrix and hidden bias vector are enough for calculating activities
input_size = net_structure[each_layer]
activity_fh = open(activity_file, 'w')
raw_activity_fh = open(raw_activity_file,'w')
for each_layer in xrange(len(net_structure)):
activity_fh.write('layer %i \n' %(each_layer+1))
raw_activity_fh.write('layer %i \n' %(each_layer+1))
temp = input_data * layer_para[each_layer][0] #calculate raw activity, before sigmoid transformation
output = logit(temp+ layer_para[each_layer][1].T) #calculate activity, after sigmoid transformation
for each_sample in xrange(output.shape[0]):
activity_fh.write(sample_id[each_sample]+'\t')
raw_activity_fh.write(sample_id[each_sample]+'\t')
numpy.savetxt(activity_fh, output[each_sample,], fmt= '%.8f', delimiter= '\t')
numpy.savetxt(raw_activity_fh, temp[each_sample,], fmt= '%.8f', delimiter= '\t')
input_data = output
