void do_more_stdin(FILE *fp)
{
char buffer[BUFFER_SIZE];
char *numByte;
int wordSize;
int toDelete;
int linesDone = 0;
while(!feof(fp))
{
if(fgets(buffer, BUFFER_SIZE, fp))
{
if(linesDone > 0)
deleteSpace(toDelete + SIZE);
numByte = buffer;
wordSize = ((int)strlen(numByte) * sizeof(numByte) - BYTES) / BYTES;
printf("%s", buffer);
sprintf(buffer, "bytes %d", wordSize);
numByte = buffer;
printf("\033[7m %s \033[m", numByte);
toDelete = ((int)strlen(numByte) * sizeof(numByte) - BYTES) / BYTES;
memset(&buffer[0], 0, sizeof(buffer));
linesDone++;
}
}
}
int see_more(FILE *cmd, char *buf)
{
int c;
printf("\033[7m %s \033[m", buf);
while((c = getc(cmd)) != EOF)
{
if(c == 'q')
{
deleteSpace((int)strlen(buf) + TWO);
return 0;
}
if(c == ' ')
{
deleteSpace((int)strlen(buf) + TWO);
return PAGELEN;
}
if(c == '\n')
{
deleteSpace((int)strlen(buf) + TWO);
return 1;
}
}
return SUCCESS;
}
void Config::init(string path,
SoftMax &SMR) {
m_configStr = read_2_string(path);
deleteComment();
deleteSpace();
get_layers_config(m_configStr,SMR);
use_log = get_word_bool(m_configStr, "USE_LOG");
batch_size = get_word_int(m_configStr, "BATCH_SIZE");
non_linearity = get_word_type(m_configStr, "NON_LINEARITY");
training_epochs = get_word_int(m_configStr, "TRAINING_EPOCHS");
lrate_w = get_word_float(m_configStr, "LRATE_W");
lrate_b = get_word_float(m_configStr, "LRATE_B");
iter_per_epo = get_word_int(m_configStr, "ITER_PER_EPO");
ngram = get_word_int(m_configStr, "NGRAM");
training_percent = get_word_float(m_configStr, "TRAINING_PERCENT");
cout
<< "****************************************************************************"
<< endl
<< "** READ CONFIG FILE COMPLETE "
<< endl
<< "****************************************************************************"
<< endl << endl;
for (int i = 0; i < HiddenConfigs.size(); i++) {
cout << "***** hidden layer: " << i << " *****" << endl;
cout << "NumHiddenNeurons = " << HiddenConfigs[i].get_NeuronNum()
<< endl;
cout << "WeightDecay = " << HiddenConfigs[i].get_WeightDecay() << endl;
cout << "DropoutRate = " << HiddenConfigs[i].get_DropoutRate() << endl << endl;
}
cout << "***** softmax layer: *****" << endl;
// cout<<"NumClasses = "<<softmaxConfig.NumClasses<<endl;
cout << "WeightDecay = " << SMR.get_WeightDecay() << endl << endl;
cout << "***** general config *****" << endl;
cout << "use_log = " << use_log << endl;
cout << "batch size = " << batch_size << endl;
cout << "non-linearity method = " << non_linearity << endl;
cout << "training epochs = " << training_epochs << endl;
cout << "learning rate for weight matrices = " << lrate_w << endl;
cout << "learning rate for bias = " << lrate_b << endl;
cout << "iteration per epoch = " << iter_per_epo << endl;
cout << "ngram = " << ngram << endl;
cout << "training percent = " << training_percent << endl;
cout << endl;
}
void Memory::initialize(const std::string & str) {
std::ifstream ifs(str);
std::string s;
if (ifs.fail()) {
std::cerr << str + " does not exist." << std::endl;
exit(0);
}
int i = 0;
while(std::getline(ifs, s)) {
s = deleteSpace(s);
s = deleteComment(s);
if (s.size() == 0) { continue; }
memory[i] = s;
i++;
}
int size = memory.size();
for (; i < size; i++) {
memory[i] = "0000000000000000";
}
}
static int CreateMaxSpaces(void)
{
int res = 1, i = 0;
int max;
L4_Word_t result, end;
L4_SpaceId_t space;
okl4_allocator_attr_t attr;
while(res == 1)
{
result = okl4_kspaceid_allocany(spaceid_pool, &space);
fail_unless(result == OKL4_OK, "Failed to allocate any space id.");
res = create_address_space(space,
L4_Fpage(0xb10000, 0x1000));
if(res != 1)
break;
/* 2gb mapping of 512k pages*/
res = map_series(space, 0x80000, 4096, 0, 0);
i++;
}
max = i;
/* clean up */
okl4_kspaceid_getattribute(spaceid_pool, &attr);
end = attr.base + attr.size;
for (i = attr.base; i < end; i++)
{
L4_SpaceId_t id = L4_SpaceId(i);
if (okl4_kspaceid_isallocated(spaceid_pool, id) &&
id.space_no != KTEST_SPACE.space_no)
{
deleteSpace(id);
}
}
return max;
}
