""" Define Deep reinforcement learning network """ self.mainQN = SSDnetwork(param.h_size,self.env,"main",param.beta,param.n_step) self.targetQN = SSDnetwork(param.h_size,self.env,"target",param.alpha,param.n_step) self.trainables = tf.trainable_variables() self.copyOps = copyGraphOp(self.trainables) self.copyOps2 = copyGraphOp2(self.trainables) self.init = tf.global_variables_initializer() self.saver = tf.train.Saver() train_model = model() init = train_model.init saver = train_model.saver seq1 = readseq.readseq('lib/Ecoli_31.txt') seq2 = readseq.readseq('lib/Ecoli_42.txt') if not os.path.exists(game_env().path): os.makedirs(game_env().path) if np.size(os.listdir(game_env().path)) > 0: resume = FLAGS.resume else: resume = False seq = readseq.readseqs('lib/HEV.txt') """ Main training step """ with tf.Session() as sess: if FLAGS.use_GPU:
self.trainables = tf.trainable_variables() self.targetOps = updateTargetGraph(self.trainables, self.param.tau) elif FLAGS.model_name == "SSD": self.mainQN = SSDnetwork(self.param.h_size,self.env,"main",self.LEARNING_RATE,self.param.n_step) self.targetQN = SSDnetwork(self.param.h_size,self.env,"target",self.LEARNING_RATE,self.param.n_step) self.trainables = tf.trainable_variables() self.targetOps = updateTargetGraph(self.trainables, self.param.tau) self.init = tf.global_variables_initializer() self.saver = tf.train.Saver() train_model = model() init = train_model.init saver = train_model.saver seq1 = readseq.readseq('lib/Ecoli_1.txt') seq2 = readseq.readseq('lib/Ecoli_2.txt') if not os.path.exists(train_env.path): os.makedirs(train_env.path) if np.size(os.listdir(train_env.path)) > 0: resume = FLAGS.resume else: resume = False """ Main test step """ with tf.Session() as sess: if FLAGS.use_GPU: sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0})) else:
start = time.time() startdate = time.localtime() for pairs in [["Homo_sapiens_BRCA", "Mus_musculus_BRCA"], ["Mus_musculus_BRCA", "Rattus_norvegicus_BRCA"], ["Rattus_norvegicus_BRCA", "Homo_sapiens_BRCA"], ["Homo_sapiens_ELK1", "Mus_musculus_ELK1"], ["Mus_musculus_ELK1", "Rattus_norvegicus_ELK1"], ["Rattus_norvegicus_ELK1", "Homo_sapiens_ELK1"], ["Homo_sapiens_CCDC91", "Mus_musculus_CCDC91"], ["Mus_musculus_CCDC91", "Rattus_norvegicus_CCDC91"], ["Rattus_norvegicus_CCDC91", "Homo_sapiens_CCDC91"], ["Homo_sapiens_FOXP2", "Mus_musculus_FOXP2"], ["Mus_musculus_FOXP2", "Rattus_norvegicus_FOXP2"], ["Rattus_norvegicus_FOXP2", "Homo_sapiens_FOXP2"]]: seq1 = readseq.readseq('lib/Mammal/' + pairs[0] + '.txt') seq2 = readseq.readseq('lib/Mammal/' + pairs[1] + '.txt') #print(_) past = time.time() agent.set(seq1 + "A", seq2 + "A") rT1, rT2, processingtime, j = agent.Global(sess) now = time.time() #NWresult = np.max(NW.match(alignment.HEVseq[_],alignment.HEVseq[__])) print(pairs, len(seq1), len(seq2), rT2, "time", str(processingtime) + "s", str(now - start) + "s") filename = "result/" + FLAGS.model_name + "/result%04d%02d%02d%02d%02d%02d_%d_%d.txt" % ( startdate.tm_year, startdate.tm_mon, startdate.tm_mday,
self.mainQN = SSDnetwork(self.param.h_size, self.env, "main", self.LEARNING_RATE, self.param.n_step) self.targetQN = SSDnetwork(self.param.h_size, self.env, "target", self.LEARNING_RATE, self.param.n_step) self.trainables = tf.trainable_variables() self.targetOps = updateTargetGraph(self.trainables, self.param.tau) self.init = tf.global_variables_initializer() self.saver = tf.train.Saver() train_model = model() init = train_model.init saver = train_model.saver seq1 = readseq.readseq('lib/Rattus_norvegicus.Rnor_6.0.dna_rm.chromosome.X.fa') seq2 = readseq.readseq('lib/Mus_musculus.GRCm38.dna_rm.chromosome.X.fa') if not os.path.exists(train_env.path): os.makedirs(train_env.path) if np.size(os.listdir(train_env.path)) > 0: resume = FLAGS.resume else: resume = False """ Main test step """ with tf.Session() as sess: if FLAGS.use_GPU: sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0})) else: sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
import numpy as np import DQNalign.tool.Bio.conventional as conventional import DQNalign.tool.util.ReadSeq as readseq import time import tensorflow as tf import DQNalign.flags as flags FLAGS = tf.app.flags.FLAGS start = time.time() startdate = time.localtime() seq1 = readseq.readseq('lib/Prochlorococcus_1.fna') seq2 = readseq.readseq('lib/Prochlorococcus_2.fna') past = time.time() c = conventional.Clustal(True, seq1, seq2) match = c.pair_align() now = time.time() print("Ecoli test") print("result", match, "time", str(now - past) + "s", str(now - start) + "s") filename = "result/Clustal/result%04d%02d%02d%02d%02d%02d_Ecoli.txt" % ( startdate.tm_year, startdate.tm_mon, startdate.tm_mday, startdate.tm_hour, startdate.tm_min, startdate.tm_sec) file = open(filename, "a") file.write(str(match) + " " + str(now - past) + " " + str(now - start) + "\n") file.close() filename = "img/Clustal/result%04d%02d%02d%02d%02d%02d_Ecoli" % (