#define NULL ((void *)0)
typedef unsigned long size_t; // Customize by platform.
typedef long intptr_t;
typedef unsigned long uintptr_t;
typedef long scalar_t__; // Either arithmetic or pointer type.
/* By default, we understand bool (as a convenience). */
typedef int bool;
#define false 0
#define true 1

/* Forward declarations */

/* Type definitions */

/* Variables and functions */
int /*<<< orphan*/ puts(char *);
void display_help(void) {
  puts("rnn-learn  - an implementation of a gradient-based learning "
       "algorithm for recurrent neural networks focusing on the problem "
       "of time-series prediction and generation");
  puts("");
  puts("Usage: rnn-learn [-s seed] [-n neurons] [-t time-constant] "
       "[-d feedback-delay] [-k output-type] [-e epochs] "
       "[-l log-interval] [-x learning-rate] [-m momentum] [-a] "
       "[-p prior-strength] [-i input-file] [-o output-file] "
       "[-c config-file] [-V] [target-file ...]");
  puts("Usage: rnn-learn [-v] [-h]");
  puts("");
  puts("Available options are:");
  puts("-s seed");
  puts("    `seed' is the seed for the initialization of random number "
       "generator, which specifies a starting point for the random number "
       "sequence, and provides for restarting at the same point. If this "
       "option is omitted, the current system time is used.");
  puts("-n neurons");
  puts("    Number of context neurons in recurrent a neural network. "
       "Default is "
       "10"
       ".");
  puts("-t time-constant");
  puts("    Time constant for each neuron primarily determines the time "
       "scale of the activation dynamics of the neuron. Default is "
       "5"
       ".");
  puts("-d feedback-delay");
  puts("    Time delay in a self-feedback from output to input. This "
       "parameter also means that a model learns to predict future values "
       "of the time series from past values with the delay interval. "
       "Default is "
       "1"
       ".");
  puts("-k output-type");
  puts("    Type of output function");
  puts("        0: tanh (Default)");
  puts("        1: softmax activation function");
  puts("-e epochs");
  puts("    Number of the training iterations to optimize model parameters. "
       "Default is "
       "1000"
       ".");
  puts("-l log-interval");
  puts("    `log-interval' is the learning step between data samples being "
       "logged. Default is "
       "100"
       ".");
  puts("-x learning-rate");
  puts("    Learning rate for the gradient-descent method. "
       "Default is "
       "0.001"
       ".");
  puts("-m momentum");
  puts("    Learning momentum for the gradient-descent method. "
       "Default is "
       "0.9"
       ".");
  puts("-a");
  puts("    Use an algorithm to update the learning rate adaptively with "
       "respect to the error.");
  puts("-p prior-strength");
  puts("    Effect of the normal prior distribution over the parameter "
       "space. Default is "
       "0"
       ". (Note: setting "
       "`prior-strength' <= 0.001 is recommended.)");
  puts("-i input-file");
  puts("    The input file `input-file' being generated by rnn-learn is used "
       "for the initialization of model parameters. If there are no "
       "target-files named on the command line, rnn-learn restarts "
       "training at the end of previous trial. Otherwise rnn-learn "
       "resets training examples by means of the files.");
  puts("-o output-file");
  puts("    The optimized model parameters are written to `output-file'. "
       "Default is `"
       "rnn.dat"
       "'.");
  puts("-c config-file");
  puts("    The configuration file `config-file' is read.");
  puts("-V");
  puts("    Verbose.");
  puts("-v");
  puts("    Prints the version information and exit.");
  puts("-h");
  puts("    Prints this help and exit.");
  puts("");
  puts("Program execution:");
  puts("rnn-learn first reads parameters from the command line, or from a "
       "configuration file. Next, rnn-learn proceeds to read each "
       "target-file which contains training examples. If there are no "
       "target-files named on the command line, rnn-learn reads the "
       "standard input. After all the input from target-files is "
       "exhausted, rnn-learn executes training iterations.");
  puts("");
  puts("Target-file format which rnn-learn can recognize:");
  puts("In a target-file, the data columns are separated by a space, tab or "
       "comma. The number of data columns means the dimension of time "
       "series of training examples. Comments begin at a sign \"#\" and "
       "continue to the end of the line. If data are separated by a blank "
       "line, each data block is recognized as a different time series.");
}


x86 assembly (clang, -O0):
<display_help>:
push   %rbp
mov    %rsp,%rbp
lea    0xee5(%rip),%rdi
call   1030 <puts@plt>
lea    0x1649(%rip),%rdi
call   1030 <puts@plt>
lea    0xf72(%rip),%rdi
call   1030 <puts@plt>
lea    0x1067(%rip),%rdi
call   1030 <puts@plt>
lea    0x1625(%rip),%rdi
call   1030 <puts@plt>
lea    0x106a(%rip),%rdi
call   1030 <puts@plt>
lea    0x1075(%rip),%rdi
call   1030 <puts@plt>
lea    0x1071(%rip),%rdi
call   1030 <puts@plt>
lea    0x115b(%rip),%rdi
call   1030 <puts@plt>
lea    0x115a(%rip),%rdi
call   1030 <puts@plt>
lea    0x119a(%rip),%rdi
call   1030 <puts@plt>
lea    0x119f(%rip),%rdi
call   1030 <puts@plt>
lea    0x1211(%rip),%rdi
call   1030 <puts@plt>
lea    0x1217(%rip),%rdi
call   1030 <puts@plt>
lea    0x12d5(%rip),%rdi
call   1030 <puts@plt>
lea    0x12d8(%rip),%rdi
call   1030 <puts@plt>
lea    0x12e8(%rip),%rdi
call   1030 <puts@plt>
lea    0x12f6(%rip),%rdi
call   1030 <puts@plt>
lea    0x1311(%rip),%rdi
call   1030 <puts@plt>
lea    0x130f(%rip),%rdi
call   1030 <puts@plt>
lea    0x1358(%rip),%rdi
call   1030 <puts@plt>
lea    0x135c(%rip),%rdi
call   1030 <puts@plt>
lea    0x13ab(%rip),%rdi
call   1030 <puts@plt>
lea    0x13b0(%rip),%rdi
call   1030 <puts@plt>
lea    0x13e9(%rip),%rdi
call   1030 <puts@plt>
lea    0x13e9(%rip),%rdi
call   1030 <puts@plt>
lea    0x1424(%rip),%rdi
call   1030 <puts@plt>
lea    0x141b(%rip),%rdi
call   1030 <puts@plt>
lea    0x1466(%rip),%rdi
call   1030 <puts@plt>
lea    0x146c(%rip),%rdi
call   1030 <puts@plt>
lea    0x14ee(%rip),%rdi
call   1030 <puts@plt>
lea    0x14f0(%rip),%rdi
call   1030 <puts@plt>
lea    0x160b(%rip),%rdi
call   1030 <puts@plt>
lea    0x160e(%rip),%rdi
call   1030 <puts@plt>
lea    0x1659(%rip),%rdi
call   1030 <puts@plt>
lea    0x165c(%rip),%rdi
call   1030 <puts@plt>
lea    0x1682(%rip),%rdi
call   1030 <puts@plt>
lea    0x1679(%rip),%rdi
call   1030 <puts@plt>
lea    0x167a(%rip),%rdi
call   1030 <puts@plt>
lea    0x1671(%rip),%rdi
call   1030 <puts@plt>
lea    0x1692(%rip),%rdi
call   1030 <puts@plt>
lea    0x1689(%rip),%rdi
call   1030 <puts@plt>
lea    0x145d(%rip),%rdi
call   1030 <puts@plt>
lea    0x1690(%rip),%rdi
call   1030 <puts@plt>
lea    0x1697(%rip),%rdi
call   1030 <puts@plt>
lea    0x1439(%rip),%rdi
call   1030 <puts@plt>
lea    0x17e1(%rip),%rdi
call   1030 <puts@plt>
lea    0x1807(%rip),%rdi
call   1030 <puts@plt>
pop    %rbp
ret

x86 assembly (clang, -O2):
<display_help>:
push   %rax
lea    0xed8(%rip),%rdi
call   1040 <puts@plt>
mov    $0xa,%edi
call   1030 <putchar@plt>
lea    0xf67(%rip),%rdi
call   1040 <puts@plt>
lea    0x105c(%rip),%rdi
call   1040 <puts@plt>
mov    $0xa,%edi
call   1030 <putchar@plt>
lea    0x1061(%rip),%rdi
call   1040 <puts@plt>
lea    0x106c(%rip),%rdi
call   1040 <puts@plt>
lea    0x1068(%rip),%rdi
call   1040 <puts@plt>
lea    0x1152(%rip),%rdi
call   1040 <puts@plt>
lea    0x1151(%rip),%rdi
call   1040 <puts@plt>
lea    0x1191(%rip),%rdi
call   1040 <puts@plt>
lea    0x1196(%rip),%rdi
call   1040 <puts@plt>
lea    0x1208(%rip),%rdi
call   1040 <puts@plt>
lea    0x120e(%rip),%rdi
call   1040 <puts@plt>
lea    0x12cc(%rip),%rdi
call   1040 <puts@plt>
lea    0x12cf(%rip),%rdi
call   1040 <puts@plt>
lea    0x12df(%rip),%rdi
call   1040 <puts@plt>
lea    0x12ed(%rip),%rdi
call   1040 <puts@plt>
lea    0x1308(%rip),%rdi
call   1040 <puts@plt>
lea    0x1306(%rip),%rdi
call   1040 <puts@plt>
lea    0x134f(%rip),%rdi
call   1040 <puts@plt>
lea    0x1353(%rip),%rdi
call   1040 <puts@plt>
lea    0x13a2(%rip),%rdi
call   1040 <puts@plt>
lea    0x13a7(%rip),%rdi
call   1040 <puts@plt>
lea    0x13e0(%rip),%rdi
call   1040 <puts@plt>
lea    0x13e0(%rip),%rdi
call   1040 <puts@plt>
lea    0x141b(%rip),%rdi
call   1040 <puts@plt>
lea    0x1412(%rip),%rdi
call   1040 <puts@plt>
lea    0x145d(%rip),%rdi
call   1040 <puts@plt>
lea    0x1463(%rip),%rdi
call   1040 <puts@plt>
lea    0x14e5(%rip),%rdi
call   1040 <puts@plt>
lea    0x14e7(%rip),%rdi
call   1040 <puts@plt>
lea    0x1602(%rip),%rdi
call   1040 <puts@plt>
lea    0x1605(%rip),%rdi
call   1040 <puts@plt>
lea    0x1650(%rip),%rdi
call   1040 <puts@plt>
lea    0x1653(%rip),%rdi
call   1040 <puts@plt>
lea    0x1679(%rip),%rdi
call   1040 <puts@plt>
lea    0x1670(%rip),%rdi
call   1040 <puts@plt>
lea    0x1671(%rip),%rdi
call   1040 <puts@plt>
lea    0x1668(%rip),%rdi
call   1040 <puts@plt>
lea    0x1689(%rip),%rdi
call   1040 <puts@plt>
lea    0x1680(%rip),%rdi
call   1040 <puts@plt>
mov    $0xa,%edi
call   1030 <putchar@plt>
lea    0x1689(%rip),%rdi
call   1040 <puts@plt>
lea    0x1690(%rip),%rdi
call   1040 <puts@plt>
mov    $0xa,%edi
call   1030 <putchar@plt>
lea    0x17dc(%rip),%rdi
call   1040 <puts@plt>
lea    0x1802(%rip),%rdi
pop    %rax
jmp    1040 <puts@plt>

LLVM IR (-O0):
@.str = [165 x i8] c"rnn-learn - an implementation of a gradient-based learning algorithm for recurrent neural networks focusing on the problem of time-series prediction and generation\00"
@.str.1 = [1 x i8] zeroinitializer
@.str.2 = [257 x i8] c"Usage: rnn-learn [-s seed] [-n neurons] [-t time-constant] [-d feedback-delay] [-k output-type] [-e epochs] [-l log-interval] [-x learning-rate] [-m momentum] [-a] [-p prior-strength] [-i input-file] [-o output-file] [-c config-file] [-V] [target-file ...]\00"
@.str.3 = [27 x i8] c"Usage: rnn-learn [-v] [-h]\00"
@.str.4 = [23 x i8] c"Available options are:\00"
@.str.5 = [8 x i8] c"-s seed\00"
@.str.6 = [246 x i8] c" `seed' is the seed for the initialization of random number generator, which specifies a starting point for the random number sequence, and provides for restarting at the same point. If this option is omitted, the current system time is used.\00"
@.str.7 = [11 x i8] c"-n neurons\00"
@.str.8 = [76 x i8] c" Number of context neurons in recurrent a neural network. Default is 10.\00"
@.str.9 = [17 x i8] c"-t time-constant\00"
@.str.10 = [126 x i8] c" Time constant for each neuron primarily determines the time scale of the activation dynamics of the neuron. Default is 5.\00"
@.str.11 = [18 x i8] c"-d feedback-delay\00"
@.str.12 = [202 x i8] c" Time delay in a self-feedback from output to input. This parameter also means that a model learns to predict future values of the time series from past values with the delay interval. Default is 1.\00"
@.str.13 = [15 x i8] c"-k output-type\00"
@.str.14 = [28 x i8] c" Type of output function\00"
@.str.15 = [26 x i8] c" 0: tanh (Default)\00"
@.str.16 = [39 x i8] c" 1: softmax activation function\00"
@.str.17 = [10 x i8] c"-e epochs\00"
@.str.18 = [85 x i8] c" Number of the training iterations to optimize model parameters. Default is 1000.\00"
@.str.19 = [16 x i8] c"-l log-interval\00"
@.str.20 = [91 x i8] c" `log-interval' is the learning step between data samples being logged. Default is 100.\00"
@.str.21 = [17 x i8] c"-x learning-rate\00"
@.str.22 = [69 x i8] c" Learning rate for the gradient-descent method. Default is 0.001.\00"
@.str.23 = [12 x i8] c"-m momentum\00"
@.str.24 = [71 x i8] c" Learning momentum for the gradient-descent method. Default is 0.9.\00"
@.str.25 = [3 x i8] c"-a\00"
@.str.26 = [87 x i8] c" Use an algorithm to update the learning rate adaptively with respect to the error.\00"
@.str.27 = [18 x i8] c"-p prior-strength\00"
@.str.28 = [142 x i8] c" Effect of the normal prior distribution over the parameter space. Default is 0. (Note: setting `prior-strength' <= 0.001 is recommended.)\00"
@.str.29 = [14 x i8] c"-i input-file\00"
@.str.30 = [295 x i8] c" The input file `input-file' being generated by rnn-learn is used for the initialization of model parameters. If there are no target-files named on the command line, rnn-learn restarts training at the end of previous trial. Otherwise rnn-learn resets training examples by means of the files.\00"
@.str.31 = [15 x i8] c"-o output-file\00"
@.str.32 = [87 x i8] c" The optimized model parameters are written to `output-file'. Default is `rnn.dat'.\00"
@.str.33 = [15 x i8] c"-c config-file\00"
@.str.34 = [50 x i8] c" The configuration file `config-file' is read.\00"
@.str.35 = [3 x i8] c"-V\00"
@.str.36 = [13 x i8] c" Verbose.\00"
@.str.37 = [3 x i8] c"-v\00"
@.str.38 = [45 x i8] c" Prints the version information and exit.\00"
@.str.39 = [3 x i8] c"-h\00"
@.str.40 = [31 x i8] c" Prints this help and exit.\00"
@.str.41 = [19 x i8] c"Program execution:\00"
@.str.42 = [354 x i8] c"rnn-learn first reads parameters from the command line, or from a configuration file. Next, rnn-learn proceeds to read each target-file which contains training examples. If there are no target-files named on the command line, rnn-learn reads the standard input. After all the input from target-files is exhausted, rnn-learn executes training iterations.\00"
@.str.43 = [50 x i8] c"Target-file format which rnn-learn can recognize:\00"
@.str.44 = [322 x i8] c"In a target-file, the data columns are separated by a space, tab or comma. The number of data columns means the dimension of time series of training examples. Comments begin at a sign \22#\22 and continue to the end of the line. If data are separated by a blank line, each data block is recognized as a different time series.\00"
define void @display_help() {
%1 = call i32 @puts(i8* getelementptr inbounds ([165 x i8], [165 x i8]* @.str, i64 0, i64 0))
%2 = call i32 @puts(i8* getelementptr inbounds ([1 x i8], [1 x i8]* @.str.1, i64 0, i64 0))
%3 = call i32 @puts(i8* getelementptr inbounds ([257 x i8], [257 x i8]* @.str.2, i64 0, i64 0))
%4 = call i32 @puts(i8* getelementptr inbounds ([27 x i8], [27 x i8]* @.str.3, i64 0, i64 0))
%5 = call i32 @puts(i8* getelementptr inbounds ([1 x i8], [1 x i8]* @.str.1, i64 0, i64 0))
%6 = call i32 @puts(i8* getelementptr inbounds ([23 x i8], [23 x i8]* @.str.4, i64 0, i64 0))
%7 = call i32 @puts(i8* getelementptr inbounds ([8 x i8], [8 x i8]* @.str.5, i64 0, i64 0))
%8 = call i32 @puts(i8* getelementptr inbounds ([246 x i8], [246 x i8]* @.str.6, i64 0, i64 0))
%9 = call i32 @puts(i8* getelementptr inbounds ([11 x i8], [11 x i8]* @.str.7, i64 0, i64 0))
%10 = call i32 @puts(i8* getelementptr inbounds ([76 x i8], [76 x i8]* @.str.8, i64 0, i64 0))
%11 = call i32 @puts(i8* getelementptr inbounds ([17 x i8], [17 x i8]* @.str.9, i64 0, i64 0))
%12 = call i32 @puts(i8* getelementptr inbounds ([126 x i8], [126 x i8]* @.str.10, i64 0, i64 0))
%13 = call i32 @puts(i8* getelementptr inbounds ([18 x i8], [18 x i8]* @.str.11, i64 0, i64 0))
%14 = call i32 @puts(i8* getelementptr inbounds ([202 x i8], [202 x i8]* @.str.12, i64 0, i64 0))
%15 = call i32 @puts(i8* getelementptr inbounds ([15 x i8], [15 x i8]* @.str.13, i64 0, i64 0))
%16 = call i32 @puts(i8* getelementptr inbounds ([28 x i8], [28 x i8]* @.str.14, i64 0, i64 0))
%17 = call i32 @puts(i8* getelementptr inbounds ([26 x i8], [26 x i8]* @.str.15, i64 0, i64 0))
%18 = call i32 @puts(i8* getelementptr inbounds ([39 x i8], [39 x i8]* @.str.16, i64 0, i64 0))
%19 = call i32 @puts(i8* getelementptr inbounds ([10 x i8], [10 x i8]* @.str.17, i64 0, i64 0))
%20 = call i32 @puts(i8* getelementptr inbounds ([85 x i8], [85 x i8]* @.str.18, i64 0, i64 0))
%21 = call i32 @puts(i8* getelementptr inbounds ([16 x i8], [16 x i8]* @.str.19, i64 0, i64 0))
%22 = call i32 @puts(i8* getelementptr inbounds ([91 x i8], [91 x i8]* @.str.20, i64 0, i64 0))
%23 = call i32 @puts(i8* getelementptr inbounds ([17 x i8], [17 x i8]* @.str.21, i64 0, i64 0))
%24 = call i32 @puts(i8* getelementptr inbounds ([69 x i8], [69 x i8]* @.str.22, i64 0, i64 0))
%25 = call i32 @puts(i8* getelementptr inbounds ([12 x i8], [12 x i8]* @.str.23, i64 0, i64 0))
%26 = call i32 @puts(i8* getelementptr inbounds ([71 x i8], [71 x i8]* @.str.24, i64 0, i64 0))
%27 = call i32 @puts(i8* getelementptr inbounds ([3 x i8], [3 x i8]* @.str.25, i64 0, i64 0))
%28 = call i32 @puts(i8* getelementptr inbounds ([87 x i8], [87 x i8]* @.str.26, i64 0, i64 0))
%29 = call i32 @puts(i8* getelementptr inbounds ([18 x i8], [18 x i8]* @.str.27, i64 0, i64 0))
%30 = call i32 @puts(i8* getelementptr inbounds ([142 x i8], [142 x i8]* @.str.28, i64 0, i64 0))
%31 = call i32 @puts(i8* getelementptr inbounds ([14 x i8], [14 x i8]* @.str.29, i64 0, i64 0))
%32 = call i32 @puts(i8* getelementptr inbounds ([295 x i8], [295 x i8]* @.str.30, i64 0, i64 0))
%33 = call i32 @puts(i8* getelementptr inbounds ([15 x i8], [15 x i8]* @.str.31, i64 0, i64 0))
%34 = call i32 @puts(i8* getelementptr inbounds ([87 x i8], [87 x i8]* @.str.32, i64 0, i64 0))
%35 = call i32 @puts(i8* getelementptr inbounds ([15 x i8], [15 x i8]* @.str.33, i64 0, i64 0))
%36 = call i32 @puts(i8* getelementptr inbounds ([50 x i8], [50 x i8]* @.str.34, i64 0, i64 0))
%37 = call i32 @puts(i8* getelementptr inbounds ([3 x i8], [3 x i8]* @.str.35, i64 0, i64 0))
%38 = call i32 @puts(i8* getelementptr inbounds ([13 x i8], [13 x i8]* @.str.36, i64 0, i64 0))
%39 = call i32 @puts(i8* getelementptr inbounds ([3 x i8], [3 x i8]* @.str.37, i64 0, i64 0))
%40 = call i32 @puts(i8* getelementptr inbounds ([45 x i8], [45 x i8]* @.str.38, i64 0, i64 0))
%41 = call i32 @puts(i8* getelementptr inbounds ([3 x i8], [3 x i8]* @.str.39, i64 0, i64 0))
%42 = call i32 @puts(i8* getelementptr inbounds ([31 x i8], [31 x i8]* @.str.40, i64 0, i64 0))
%43 = call i32 @puts(i8* getelementptr inbounds ([1 x i8], [1 x i8]* @.str.1, i64 0, i64 0))
%44 = call i32 @puts(i8* getelementptr inbounds ([19 x i8], [19 x i8]* @.str.41, i64 0, i64 0))
%45 = call i32 @puts(i8* getelementptr inbounds ([354 x i8], [354 x i8]* @.str.42, i64 0, i64 0))
%46 = call i32 @puts(i8* getelementptr inbounds ([1 x i8], [1 x i8]* @.str.1, i64 0, i64 0))
%47 = call i32 @puts(i8* getelementptr inbounds ([50 x i8], [50 x i8]* @.str.43, i64 0, i64 0))
%48 = call i32 @puts(i8* getelementptr inbounds ([322 x i8], [322 x i8]* @.str.44, i64 0, i64 0))
ret void
}

LLVM IR (-O2):
@.str = [165 x i8] c"rnn-learn - an implementation of a gradient-based learning algorithm for recurrent neural networks focusing on the problem of time-series prediction and generation\00"
@.str.2 = [257 x i8] c"Usage: rnn-learn [-s seed] [-n neurons] [-t time-constant] [-d feedback-delay] [-k output-type] [-e epochs] [-l log-interval] [-x learning-rate] [-m momentum] [-a] [-p prior-strength] [-i input-file] [-o output-file] [-c config-file] [-V] [target-file ...]\00"
@.str.3 = [27 x i8] c"Usage: rnn-learn [-v] [-h]\00"
@.str.4 = [23 x i8] c"Available options are:\00"
@.str.5 = [8 x i8] c"-s seed\00"
@.str.6 = [246 x i8] c" `seed' is the seed for the initialization of random number generator, which specifies a starting point for the random number sequence, and provides for restarting at the same point. If this option is omitted, the current system time is used.\00"
@.str.7 = [11 x i8] c"-n neurons\00"
@.str.8 = [76 x i8] c" Number of context neurons in recurrent a neural network. Default is 10.\00"
@.str.9 = [17 x i8] c"-t time-constant\00"
@.str.10 = [126 x i8] c" Time constant for each neuron primarily determines the time scale of the activation dynamics of the neuron. Default is 5.\00"
@.str.11 = [18 x i8] c"-d feedback-delay\00"
@.str.12 = [202 x i8] c" Time delay in a self-feedback from output to input. This parameter also means that a model learns to predict future values of the time series from past values with the delay interval. Default is 1.\00"
@.str.13 = [15 x i8] c"-k output-type\00"
@.str.14 = [28 x i8] c" Type of output function\00"
@.str.15 = [26 x i8] c" 0: tanh (Default)\00"
@.str.16 = [39 x i8] c" 1: softmax activation function\00"
@.str.17 = [10 x i8] c"-e epochs\00"
@.str.18 = [85 x i8] c" Number of the training iterations to optimize model parameters. Default is 1000.\00"
@.str.19 = [16 x i8] c"-l log-interval\00"
@.str.20 = [91 x i8] c" `log-interval' is the learning step between data samples being logged. Default is 100.\00"
@.str.21 = [17 x i8] c"-x learning-rate\00"
@.str.22 = [69 x i8] c" Learning rate for the gradient-descent method. Default is 0.001.\00"
@.str.23 = [12 x i8] c"-m momentum\00"
@.str.24 = [71 x i8] c" Learning momentum for the gradient-descent method. Default is 0.9.\00"
@.str.25 = [3 x i8] c"-a\00"
@.str.26 = [87 x i8] c" Use an algorithm to update the learning rate adaptively with respect to the error.\00"
@.str.27 = [18 x i8] c"-p prior-strength\00"
@.str.28 = [142 x i8] c" Effect of the normal prior distribution over the parameter space. Default is 0. (Note: setting `prior-strength' <= 0.001 is recommended.)\00"
@.str.29 = [14 x i8] c"-i input-file\00"
@.str.30 = [295 x i8] c" The input file `input-file' being generated by rnn-learn is used for the initialization of model parameters. If there are no target-files named on the command line, rnn-learn restarts training at the end of previous trial. Otherwise rnn-learn resets training examples by means of the files.\00"
@.str.31 = [15 x i8] c"-o output-file\00"
@.str.32 = [87 x i8] c" The optimized model parameters are written to `output-file'. Default is `rnn.dat'.\00"
@.str.33 = [15 x i8] c"-c config-file\00"
@.str.34 = [50 x i8] c" The configuration file `config-file' is read.\00"
@.str.35 = [3 x i8] c"-V\00"
@.str.36 = [13 x i8] c" Verbose.\00"
@.str.37 = [3 x i8] c"-v\00"
@.str.38 = [45 x i8] c" Prints the version information and exit.\00"
@.str.39 = [3 x i8] c"-h\00"
@.str.40 = [31 x i8] c" Prints this help and exit.\00"
@.str.41 = [19 x i8] c"Program execution:\00"
@.str.42 = [354 x i8] c"rnn-learn first reads parameters from the command line, or from a configuration file. Next, rnn-learn proceeds to read each target-file which contains training examples. If there are no target-files named on the command line, rnn-learn reads the standard input. After all the input from target-files is exhausted, rnn-learn executes training iterations.\00"
@.str.43 = [50 x i8] c"Target-file format which rnn-learn can recognize:\00"
@.str.44 = [322 x i8] c"In a target-file, the data columns are separated by a space, tab or comma. The number of data columns means the dimension of time series of training examples. Comments begin at a sign \22#\22 and continue to the end of the line. If data are separated by a blank line, each data block is recognized as a different time series.\00"
define void @display_help() {
%1 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([165 x i8], [165 x i8]* @.str, i64 0, i64 0))
%2 = tail call i32 @putchar(i32 10)
%3 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([257 x i8], [257 x i8]* @.str.2, i64 0, i64 0))
%4 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([27 x i8], [27 x i8]* @.str.3, i64 0, i64 0))
%5 = tail call i32 @putchar(i32 10)
%6 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([23 x i8], [23 x i8]* @.str.4, i64 0, i64 0))
%7 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([8 x i8], [8 x i8]* @.str.5, i64 0, i64 0))
%8 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([246 x i8], [246 x i8]* @.str.6, i64 0, i64 0))
%9 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([11 x i8], [11 x i8]* @.str.7, i64 0, i64 0))
%10 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([76 x i8], [76 x i8]* @.str.8, i64 0, i64 0))
%11 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([17 x i8], [17 x i8]* @.str.9, i64 0, i64 0))
%12 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([126 x i8], [126 x i8]* @.str.10, i64 0, i64 0))
%13 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([18 x i8], [18 x i8]* @.str.11, i64 0, i64 0))
%14 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([202 x i8], [202 x i8]* @.str.12, i64 0, i64 0))
%15 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([15 x i8], [15 x i8]* @.str.13, i64 0, i64 0))
%16 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([28 x i8], [28 x i8]* @.str.14, i64 0, i64 0))
%17 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([26 x i8], [26 x i8]* @.str.15, i64 0, i64 0))
%18 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([39 x i8], [39 x i8]* @.str.16, i64 0, i64 0))
%19 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([10 x i8], [10 x i8]* @.str.17, i64 0, i64 0))
%20 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([85 x i8], [85 x i8]* @.str.18, i64 0, i64 0))
%21 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([16 x i8], [16 x i8]* @.str.19, i64 0, i64 0))
%22 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([91 x i8], [91 x i8]* @.str.20, i64 0, i64 0))
%23 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([17 x i8], [17 x i8]* @.str.21, i64 0, i64 0))
%24 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([69 x i8], [69 x i8]* @.str.22, i64 0, i64 0))
%25 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([12 x i8], [12 x i8]* @.str.23, i64 0, i64 0))
%26 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([71 x i8], [71 x i8]* @.str.24, i64 0, i64 0))
%27 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([3 x i8], [3 x i8]* @.str.25, i64 0, i64 0))
%28 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([87 x i8], [87 x i8]* @.str.26, i64 0, i64 0))
%29 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([18 x i8], [18 x i8]* @.str.27, i64 0, i64 0))
%30 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([142 x i8], [142 x i8]* @.str.28, i64 0, i64 0))
%31 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([14 x i8], [14 x i8]* @.str.29, i64 0, i64 0))
%32 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([295 x i8], [295 x i8]* @.str.30, i64 0, i64 0))
%33 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([15 x i8], [15 x i8]* @.str.31, i64 0, i64 0))
%34 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([87 x i8], [87 x i8]* @.str.32, i64 0, i64 0))
%35 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([15 x i8], [15 x i8]* @.str.33, i64 0, i64 0))
%36 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([50 x i8], [50 x i8]* @.str.34, i64 0, i64 0))
%37 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([3 x i8], [3 x i8]* @.str.35, i64 0, i64 0))
%38 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([13 x i8], [13 x i8]* @.str.36, i64 0, i64 0))
%39 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([3 x i8], [3 x i8]* @.str.37, i64 0, i64 0))
%40 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([45 x i8], [45 x i8]* @.str.38, i64 0, i64 0))
%41 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([3 x i8], [3 x i8]* @.str.39, i64 0, i64 0))
%42 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([31 x i8], [31 x i8]* @.str.40, i64 0, i64 0))
%43 = tail call i32 @putchar(i32 10)
%44 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([19 x i8], [19 x i8]* @.str.41, i64 0, i64 0))
%45 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([354 x i8], [354 x i8]* @.str.42, i64 0, i64 0))
%46 = tail call i32 @putchar(i32 10)
%47 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([50 x i8], [50 x i8]* @.str.43, i64 0, i64 0))
%48 = tail call i32 @puts(i8* dereferenceable(1) getelementptr inbounds ([322 x i8], [322 x i8]* @.str.44, i64 0, i64 0))
ret void
}