#はじめに
　この手法はまだ良い結果が得られていません．趣味としてアイデアを試している段階ですので，直ぐに使えるツールをお探しの方のお役には立てないと思います．ご了承下さい．m(__)m
　さて，前回記事ではスペクトログラムという音分析技術で為替データ(USD/JPY)を画像化し，CNNで学習しました．10枚のスペクトログラムと１分後の終値の値動き（上がるor下がる）を１つのデータセットとし，過去の為替データから学習データ：57864個，テストデータ：1453個を用意しました．

図１．前回使用した学習データの例

　前回の結果が図２になります．テストデータに対する予測精度は全然上がりませんでした．

図２．前回の精度検証結果

#前回と違うところ
　今回も基本的な考え方は同じですが，以下の２点が異なります．

####学習データの調整
　表１に変更点をまとめました．１番大きな違いはFFTのサンプリング数を増やして，より低い周波数の波に分解したことです．低周波に着目した理由は，前回作成したスペクトログラムを見ると，桁違いに強い応答が出ていたからです．
　また，使用するデータを終値のみとしました．低周波まで分解したことで，スペクトログラムのサイズが大きくなり，メモリが足りなくなってしまいました(笑)．

表１．学習データの比較

図３．今回使用した学習データの例

####Tensor Flowを使用
　スペクトログラムのサイズが大きくなったことで，学習に１週間以上掛かる様になりました．ハイパーパラメータの条件出しは本番よりも軽い計算条件で行いますが，それでも時間が掛かり過ぎます．そこで，Tensor FlowのGPU環境に乗り換えました．速いです．１晩で学習が終わる様になりました．

#計算フロー

　CNNの構造を図４に示します．学習フローは図５になります．

図４．CNNの構造

図５．学習フロー

#計算結果

　今回もテストデータに対する正解率が上がりませんでした．．．残念！

#所感
####他の金融データも学習データに取り入れたい
　例えば，USD/JPYを予測するのにUSD/JPYだけでなく，EUR/JPYも考慮してみる，というアプローチは必要かなぁ，と思っています．通貨だけでなく，株式や国債，貴金属の価格なども影響していると思います．全部ぶっこみたいところですが，計算コストがどんどん増大することがネックですね．(^_^;)

####そもそも為替データをFFTすることにどんな物理的意味があるの？
　この取組では以下の仮定が正しいか検証しているのですが，
　「為替データをsin波の重ね合わせで表現した時に，各波の強さと位相，そしてその履歴は将来の為替の値動きと相関がある．」
そもそも為替データをFFTすることにどんな物理的な意味があるのでしょうか？あんまり考えてませんでした(笑)．過去の文献に何か情報無いか調べてみます．

Yu-Nie

#Appendix

　分析対象とした為替データは以下からダウンロードできます．

USDJPY_20070301_20170228_1min.csv
USDJPY_20170301_20170531_1min.csv

　以下は分析に使用したコードです．

Jack_for_qiita_TF_version.py


# 20170719
# y.izumi

import tensorflow as tf
import numpy as np
import spectrogram as spec # FFTとスペクトログラム作成を行うためのモジュール
import time

"""パラメータの初期化と畳み込み演算，pooling演算を行う関数"""
#=============================================================================================================================================
# 重みの初期化関数
def weight_variable(shape, stddev=1e-5): # default stddev = 1e-4
    initial = tf.truncated_normal(shape, stddev=stddev)
    return tf.Variable(initial)
# バイアスの初期化関数
def bias_variable(shape):
    initial = tf.constant(0.0, shape=shape)
    return tf.Variable(initial)
# 畳み込み演算
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
# pooling
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
#=============================================================================================================================================

"""スペクトログラムの作成条件"""
#=============================================================================================================================================
tr_raw_file_name = "USDJPY_20070301_20170228_1min.csv"                     # 生データファイル名 train
tr_spectrogram_file_name = "USDJPY_20070301_20170228_1min_spectrogram.csv" # FFT結果のファイル名 train
te_raw_file_name = "USDJPY_20170301_20170531_1min.csv"                     # 生データファイル名 test
te_spectrogram_file_name = "USDJPY_20170301_20170531_1min_spectrogram.csv" # FFT結果のファイル名 test
FFT_num_sample = 2048                   # 1回のFFTに使用するサンプル数（注意）2の乗数にすること
FFT_over_lap = 16                       # FFT_time_inc = FFT_num_sample / FFT_over_lap, FFT_time_incはFFTの開始時刻の増分
predict_time_inc = 15                   # 15分後の為替が上がるor下がるを予測する
time_lines = 8                          # 1枚のスペクトログラムに含まれるFFTの数
freq_lines = int(FFT_num_sample / 2)    # 1枚のスペクトログラムに踏まれる周波数の数
input_dim = (2, time_lines, freq_lines) # channel(= 2, 4, 6, 8, 10), height(time), width(freq_lines)
ch_flag = input_dim[0]
# 各チャンネルは以下の値からスペクトログラムを作成します
# ch_flag = 2 : (close) x (real, imag)
# ch_flag = 4 : (close, volume) x (real, imag)
# ch_flag = 6 : (high, low, close) x (real, imag)
# ch_flag = 8 : (start, high, low, close) x (real, imag)
# ch_flag = 10 : (start, high, low, close, volume) x (real, imag)
#==============================================================================================================================================

"""FFTの実行"""
#==============================================================================================================================================
# train data
spec.carry_out_FFT_complex(tr_raw_file_name, tr_spectrogram_file_name, FFT_num_sample, FFT_over_lap, ch_flag, predict_time_inc)
# test data
spec.carry_out_FFT_complex(te_raw_file_name, te_spectrogram_file_name, FFT_num_sample, FFT_over_lap, ch_flag, predict_time_inc)
#==============================================================================================================================================

"""スペクトログラムの作成"""
#==============================================================================================================================================
# 学習データ
print("loading traning data")
x_train, t_train = spec.merge_spectrogram(tr_spectrogram_file_name, input_dim, ch_flag)
# x_train, t_train = spec.merge_spectrogram(te_spectrogram_file_name, input_dim, ch_flag) # for debag
# テストデータ
print("loading test data")
x_test, t_test = spec.merge_spectrogram(te_spectrogram_file_name, input_dim, ch_flag)

print("x train shape " + str(x_train.shape))
print("t train shape " + str(t_train.shape))
print("x test shape " + str(x_test.shape))
print("t test shape " + str(t_test.shape))
#==============================================================================================================================================

"""データ形状の加工"""
#==============================================================================================================================================
# tensorflow用に次元を入れ替える
x_train = x_train.transpose(0, 2, 3, 1) # (num_data, ch, height(time), width(freq_lines)) ⇒ (num_data, height(time), width(freq_lines), ch)
x_test = x_test.transpose(0, 2, 3, 1)

train_size = x_train.shape[0]   # 学習データ数
test_size = x_test.shape[0]     # テストデータ数
batch_size = 200                # 学習バッチサイズ

# labes to one-hot
t_train_onehot = np.zeros((train_size, 2))
t_test_onehot = np.zeros((test_size, 2))
t_train_onehot[np.arange(train_size), t_train] = 1
t_test_onehot[np.arange(test_size), t_test] = 1
t_train = t_train_onehot
t_test = t_test_onehot

# print("t train shape onehot" + str(t_train.shape)) # for debag
# print("t test shape onehot" + str(t_test.shape))
#==============================================================================================================================================

"""CNNの構築"""
#==============================================================================================================================================
x  = tf.placeholder(tf.float32, [None, input_dim[1], input_dim[2], input_dim[0]]) # (num_data, height(time), width(freq_lines), ch)
y_ = tf.placeholder(tf.float32, [None, 2]) # (num_data, num_label)
print("input shape ", str(x.get_shape()))

with tf.variable_scope("conv1") as scope:
    W_conv1 = weight_variable([5, 5, input_dim[0], 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    print("conv1 shape ", str(h_pool1.get_shape()))

with tf.variable_scope("conv2") as scope:
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)
    print("conv2 shape ", str(h_pool2.get_shape()))
    h_pool2_height = int(h_pool2.get_shape()[1])
    h_pool2_width = int(h_pool2.get_shape()[2])

with tf.variable_scope("fc1") as scope:
    W_fc1 = weight_variable([h_pool2_height*h_pool2_width*64, 1024])
    b_fc1 = bias_variable([1024])
    h_pool2_flat = tf.reshape(h_pool2, [-1, h_pool2_height*h_pool2_width*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
    print("fc1 shape ", str(h_fc1.get_shape()))
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

with tf.variable_scope("fc2") as scope:
    W_fc2 = weight_variable([1024, 2])
    b_fc2 = bias_variable([2])
    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
    print("output shape ", str(y_conv.get_shape()))

# パラメータをtensorboardで可視化する
W_conv1 = tf.summary.histogram("W_conv1", W_conv1)
b_conv1 = tf.summary.histogram("b_conv1", b_conv1)
W_conv2 = tf.summary.histogram("W_conv2", W_conv2)
b_conv2 = tf.summary.histogram("b_conv2", b_conv2)
W_fc1 = tf.summary.histogram("W_fc1", W_fc1)
b_fc1 = tf.summary.histogram("b_fc1", b_fc1)
W_fc2 = tf.summary.histogram("W_fc2", W_fc2)
b_fc2 = tf.summary.histogram("b_fc2", b_fc2)
#==============================================================================================================================================

"""誤差関数の指定"""
#==============================================================================================================================================
# cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
cross_entropy = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y_conv))
loss_summary = tf.summary.scalar("loss", cross_entropy) # for tensorboard
#==============================================================================================================================================

"""optimizerの指定"""
#==============================================================================================================================================
optimizer = tf.train.AdamOptimizer(1e-4)
train_step = optimizer.minimize(cross_entropy)

# 勾配をtensorboardで可視化する
grads = optimizer.compute_gradients(cross_entropy)
dW_conv1 = tf.summary.histogram("dW_conv1", grads[0]) # for tensorboard
db_conv1 = tf.summary.histogram("db_conv1", grads[1])
dW_conv2 = tf.summary.histogram("dW_conv2", grads[2])
db_conv2 = tf.summary.histogram("db_conv2", grads[3])
dW_fc1 = tf.summary.histogram("dW_fc1", grads[4])
db_fc1 = tf.summary.histogram("db_fc1", grads[5])
dW_fc2 = tf.summary.histogram("dW_fc2", grads[6])
db_fc2 = tf.summary.histogram("db_fc2", grads[7])

# for i in range(8): # for debag
#     print(grads[i])
#==============================================================================================================================================

"""精度検証用パラメータ"""
#==============================================================================================================================================
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
accuracy_summary = tf.summary.scalar("accuracy", accuracy) # for tensorboard
#==============================================================================================================================================

"""学習の実行"""
#==============================================================================================================================================
acc_list = []            # 正解率と誤差の途中経過を保存するリスト
start_time = time.time() # 計算時間のカウント
total_cal_time = 0

with tf.Session() as sess:
    saver = tf.train.Saver()
    sess.run(tf.global_variables_initializer())

    # tensorboard用ファイルの書き出し
    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter(r"temp_result", sess.graph)
    
    for step in range(30001):
        batch_mask = np.random.choice(train_size, batch_size)
        batch_xs = x_train[batch_mask]
        batch_ys = t_train[batch_mask]

        # 学習途中の精度確認
        if step%100 == 0:
            
            cal_time = time.time() - start_time # 計算時間のカウント
            total_cal_time += cal_time
            
            # train
            train_accuracy = accuracy.eval(feed_dict={x:batch_xs, y_: batch_ys, keep_prob: 1.0})
            train_loss = cross_entropy.eval(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 1.0})
            # test
            test_accuracy = accuracy.eval(feed_dict={x: x_test, y_: t_test, keep_prob: 1.0})
            test_loss = cross_entropy.eval(feed_dict={x: x_test, y_: t_test, keep_prob: 1.0})
            print("calculation time %d sec, step %d, training accuracy %g, training loss %g, test accuracy %g, test loss %g"%(cal_time, step, train_accuracy, train_loss, test_accuracy, test_loss))
            
            acc_list.append([step, train_accuracy, test_accuracy, train_loss, test_loss])
            
            # tensorboard用ファイルの書き出し
            result = sess.run(merged, feed_dict={x:batch_xs, y_: batch_ys, keep_prob: 1.0})
            writer.add_summary(result, step)
            
            start_time = time.time()
 
        # 学習の実行
        train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})

    # テストデータに対する最終正解率
    print("test accuracy %g"%accuracy.eval(feed_dict={x: x_test, y_: t_test, keep_prob: 1.0}))
    print("total calculation time %g sec"%total_cal_time)
    
    np.savetxt(r"temp_result\acc_list.csv", acc_list, delimiter = ",") # 正解率と誤差の途中経過の書き出し
    saver.save(sess, r"temp_result\spectrogram_model.ckpt")            # 最終パラメータの書き出し
#==============================================================================================================================================

spectrogram.py


# -*- coding: utf-8 -*-
"""
Created on Thu Jul  6 09:30:11 2017

@author: izumiy
"""

import numpy as np

"""インプットと正解出力を読み込む関数================================================================================================================"""

def open_input_for_spectrogram(file_name, input_data_col):
    input_date_list = input_data_col
    input_date_tuple = tuple(input_date_list)
    input = np.loadtxt(file_name, delimiter = ",", usecols = input_date_tuple)
    
    return input

def open_label_for_spectrogram(file_name, label_col):
    label = np.loadtxt(file_name, delimiter = ",", usecols = label_col, dtype = np.int)
    
    return label

"""為替の時系列データをFFTする関数==================================================================================================================="""
"""
・為替の時系列データに対してFFTを連続実行する（実数部と虚数部を出力）
・FFTに使ったデータの直後の為替レートの増減をラベル化する
 下がる:0, 上がる:1, 変化無し:2
・上記を連続実行し，１つのnumpy配列にまとめる
"""

def FFT_complex_10(file_name, num_sample, time_start = 0, over_lap = 1, predict_time_inc = 1):
    """始値，高値，安値，終値, Volumeを使用""" 
    # num_sample:時間窓長に含まれるサンプリング点数(2の乗数にすること)
    
    freq_lines = int(num_sample / 2)         # 分析ライン数 = サンプル数 / 2　https://www.onosokki.co.jp/HP-WK/c_support/faq/fft_common/fft_analys_4.htm
    spectrogram_idx = 10 * freq_lines + 1    # 2(実数，虚数) x 5(始値，高値，安値，終値，Volume) = 10, 末尾の"+1"はラベル
    # print(freq_lines_idx)
    
    """為替時系列データの読込み"""
    print("reading the input file...")
    time_series = np.loadtxt(file_name, delimiter = ",", usecols = (2,3,4,5,6), skiprows = 1)   # 始値，高値，安値，終値，Volumeをnumpy配列として取得する
    num_series_data = time_series.shape[0]                                                      # データ数の取得 
    print("reading the input file is done")
    print("num_series_data")
    print(num_series_data)

    time_end = int(time_start + num_sample)
    time_inc = int(num_sample / over_lap)                         # FFT開始時刻の増分    

    spectrogram = np.empty((0, spectrogram_idx))

    while(time_end < (num_series_data - predict_time_inc)):
        
        print("time_start:" + str(time_start))
        
        """FFTの実行"""
        time_window = time_series[time_start:time_end, :]         # 時間窓長だけデータを抽出する
        
        """2次元配列に対してFFTを行う場合は，分析方向の指定が必要"""
        FFT = np.fft.fft(time_window, axis=0)                     # FFTの実行，結果は複素数として出力される
        
        FFT = FFT[0:freq_lines, :]                                # ナイキスト周波数以上のデータは削除する
        # FFT = np.abs(FFT)                                       # FFTの結果は複素数なので，絶対値にする
        FFT_real = np.real(FFT)                                   # FFTの実数部
        FFT_imag = np.imag(FFT)                                   # FFTの虚数部
        
        """ラベル作成"""
        # 始値を使用する場合
        current_rate = time_series[time_end, 3]                   # FFTの最終時刻の終値
        future = time_end + predict_time_inc                      # FFTの最終時刻の直後
        future_rate = time_series[future, 3]                      # FFTの最終時刻の直後の終値
        
        if current_rate >= future_rate:
            FFT_label = 0
#       elif current_rate == future_rate:
#           FFT_label = 2
        else:
            FFT_label = 1
        
        """FFT結果の結合"""
        FFT_complex = np.append(FFT_real, FFT_imag, axis=0)
        FFT_complex = FFT_complex.T
        # print("FFT_complex_shape:" + str(FFT_complex.shape))
        
        FFT_complex = FFT_complex.reshape(-1, 1)                                    # 縦ベクトルに変換する
        # print("FFT_complex_shape_1d:" + str(FFT_complex.shape))
        
        FFT_complex = np.append(FFT_complex, FFT_label)                             # ラベルの追記
        # print("FFT_complex_shape_label_add:" + str(FFT_complex.shape))                

        FFT_complex = FFT_complex.reshape(1, -1)                                    # 横ベクトルに変換する
        # print("FFT_complex_shape_label_add_transverse:" + str(FFT_complex.shape))
        
        spectrogram = np.append(spectrogram, FFT_complex, axis=0)                   # FFTの結果を追記する
        print("spectrogram_shape:" + str(spectrogram.shape))
                        
        time_start = time_start + time_inc                                          # FFTの開始時刻の更新
        time_end = int(time_start + num_sample)
        
        # print("num_series_data" + str(num_series_data))
        
    return spectrogram

def FFT_complex_8(file_name, num_sample, time_start = 0, over_lap = 1, predict_time_inc = 1):
    """始値，高値，安値，終値を使用""" 
    # num_sample:時間窓長に含まれるサンプリング点数(2の乗数にすること)
    
    freq_lines = int(num_sample / 2)        # 分析ライン数 = サンプル数 / 2　https://www.onosokki.co.jp/HP-WK/c_support/faq/fft_common/fft_analys_4.htm
    spectrogram_idx = 8 * freq_lines + 1    # 2(実数，虚数) x 4(始値，高値，安値，終値) = 8, 末尾の"+1"はラベル
    # print(freq_lines_idx)
    
    """為替時系列データの読込み"""
    print("reading the input file...")
    time_series = np.loadtxt(file_name, delimiter = ",", usecols = (2,3,4,5), skiprows = 1)   # 始値，高値，安値，終値をnumpy配列として取得する
    num_series_data = time_series.shape[0]                                                    # データ数の取得 
    print("reading the input file is done")
    print("num_series_data")
    print(num_series_data)

    time_end = int(time_start + num_sample)
    time_inc = int(num_sample / over_lap)                         # FFT開始時刻の増分   

    spectrogram = np.empty((0, spectrogram_idx))

    while(time_end < (num_series_data - predict_time_inc)):
        
        print("time_start:" + str(time_start))
        
        """FFTの実行"""
        time_window = time_series[time_start:time_end, :]         # 時間窓長だけデータを抽出する
        
        """2次元配列に対してFFTを行う場合は，分析方向の指定が必要"""
        FFT = np.fft.fft(time_window, axis=0)                     # FFTの実行，結果は複素数として出力される
        
        FFT = FFT[0:freq_lines, :]                                # ナイキスト周波数以上のデータは削除する
        # FFT = np.abs(FFT)                                       # FFTの結果は複素数なので，絶対値にする
        FFT_real = np.real(FFT)                                   # FFTの実数部
        FFT_imag = np.imag(FFT)                                   # FFTの虚数部
        
        """ラベル作成"""
        current_rate = time_series[time_end, 3]                   # FFTの最終時刻の終値
        future = time_end + predict_time_inc                      # FFTの最終時刻の直後
        future_rate = time_series[future, 3]                      # FFTの最終時刻の直後の終値

        if current_rate >= future_rate:
            FFT_label = 0
#       elif current_rate == future_rate:
#           FFT_label = 2
        else:
            FFT_label = 1
        
        """FFT結果の結合"""
        FFT_complex = np.append(FFT_real, FFT_imag, axis=0)
        FFT_complex = FFT_complex.T
        # print("FFT_complex_shape:" + str(FFT_complex.shape))
        
        FFT_complex = FFT_complex.reshape(-1, 1)                                    # 縦ベクトルに変換する
        # print("FFT_complex_shape_1d:" + str(FFT_complex.shape))
        
        FFT_complex = np.append(FFT_complex, FFT_label)                             # ラベルの追記
        # print("FFT_complex_shape_label_add:" + str(FFT_complex.shape))                

        FFT_complex = FFT_complex.reshape(1, -1)                                    # 横ベクトルに変換する
        # print("FFT_complex_shape_label_add_transverse:" + str(FFT_complex.shape))
        
        spectrogram = np.append(spectrogram, FFT_complex, axis=0)                   # FFTの結果を追記する
        print("spectrogram_shape:" + str(spectrogram.shape))
                        
        time_start = time_start + time_inc                                          # FFTの開始時刻の更新
        time_end = int(time_start + num_sample)
        
        # print("num_series_data" + str(num_series_data))
        
    return spectrogram

   
def FFT_complex_6(file_name, num_sample, time_start = 0, over_lap = 1, predict_time_inc = 1):
    """高値，安値，終値を使用""" 
    # num_sample:時間窓長に含まれるサンプリング点数(2の乗数にすること)
    
    freq_lines = int(num_sample / 2)        # 分析ライン数 = サンプル数 / 2　https://www.onosokki.co.jp/HP-WK/c_support/faq/fft_common/fft_analys_4.htm
    spectrogram_idx = 6 * freq_lines + 1    # 2(実数，虚数) x 3(高値，安値，終値) = 6, 末尾の"+1"はラベル
    # print(freq_lines_idx)
    
    """為替時系列データの読込み"""
    print("reading the input file...")
    time_series = np.loadtxt(file_name, delimiter = ",", usecols = (3,4,5), skiprows = 1)     # 高値，安値，終値をnumpy配列として取得する
    num_series_data = time_series.shape[0]                                                    # データ数の取得 
    print("reading the input file is done")
    print("num_series_data")
    print(num_series_data)

    time_end = int(time_start + num_sample)
    time_inc = int(num_sample / over_lap)                         # FFT開始時刻の増分 

    spectrogram = np.empty((0, spectrogram_idx))

    while(time_end < (num_series_data - predict_time_inc)):
        
        print("time_start:" + str(time_start))
        
        """FFTの実行"""
        time_window = time_series[time_start:time_end, :]         # 時間窓長だけデータを抽出する
        
        """2次元配列に対してFFTを行う場合は，分析方向の指定が必要"""
        FFT = np.fft.fft(time_window, axis=0)                     # FFTの実行，結果は複素数として出力される
        
        FFT = FFT[0:freq_lines, :]                                # ナイキスト周波数以上のデータは削除する
        # FFT = np.abs(FFT)                                       # FFTの結果は複素数なので，絶対値にする
        FFT_real = np.real(FFT)                                   # FFTの実数部
        FFT_imag = np.imag(FFT)                                   # FFTの虚数部
        
        """ラベル作成"""
        current_rate = time_series[time_end, 2]                   # FFTの最終時刻の終値
        future = time_end + predict_time_inc                      # FFTの最終時刻の直後      
        future_rate = time_series[future, 2]                      # FFTの最終時刻の直後の終値
        
        if current_rate >= future_rate:
            FFT_label = 0
#       elif current_rate == future_rate:
#           FFT_label = 2
        else:
            FFT_label = 1
        
        """FFT結果の結合"""
        FFT_complex = np.append(FFT_real, FFT_imag, axis=0)
        FFT_complex = FFT_complex.T
        # print("FFT_complex_shape:" + str(FFT_complex.shape))
        
        FFT_complex = FFT_complex.reshape(-1, 1)                                    # 縦ベクトルに変換する
        # print("FFT_complex_shape_1d:" + str(FFT_complex.shape))
        
        FFT_complex = np.append(FFT_complex, FFT_label)                             # ラベルの追記
        # print("FFT_complex_shape_label_add:" + str(FFT_complex.shape))                

        FFT_complex = FFT_complex.reshape(1, -1)                                    # 横ベクトルに変換する
        # print("FFT_complex_shape_label_add_transverse:" + str(FFT_complex.shape))
        
        spectrogram = np.append(spectrogram, FFT_complex, axis=0)                   # FFTの結果を追記する
        print("spectrogram_shape:" + str(spectrogram.shape))
                        
        time_start = time_start + time_inc                                          # FFTの開始時刻の更新
        time_end = int(time_start + num_sample)
        
        # print("num_series_data" + str(num_series_data))
        
    return spectrogram

def FFT_complex_4(file_name, num_sample, time_start = 0, over_lap = 1, predict_time_inc = 1):
    """終値,Volumeを使用""" 
    # num_sample:時間窓長に含まれるサンプリング点数(2の乗数にすること)
    
    freq_lines = int(num_sample / 2)        # 分析ライン数 = サンプル数 / 2　https://www.onosokki.co.jp/HP-WK/c_support/faq/fft_common/fft_analys_4.htm
    spectrogram_idx = 4 * freq_lines + 1    # 2(実数，虚数) x 2(終値，Volume) = 4, 末尾の"+1"はラベル
    # print(freq_lines_idx)
    
    """為替時系列データの読込み"""
    print("reading the input file...")
    time_series = np.loadtxt(file_name, delimiter = ",", usecols = (5,6), skiprows = 1)       # 終値，Volumeをnumpy配列として取得する
    num_series_data = time_series.shape[0]                                                    # データ数の取得 
    print("reading the input file is done")
    print("num_series_data")
    print(num_series_data)

    time_end = int(time_start + num_sample)
    time_inc = int(num_sample / over_lap)                         # FFT開始時刻の増分  

    spectrogram = np.empty((0, spectrogram_idx))

    while(time_end < (num_series_data - predict_time_inc)):
        
        print("time_start:" + str(time_start))
        
        """FFTの実行"""
        time_window = time_series[time_start:time_end, :]         # 時間窓長だけデータを抽出する
        
        """2次元配列に対してFFTを行う場合は，分析方向の指定が必要"""
        FFT = np.fft.fft(time_window, axis=0)                     # FFTの実行，結果は複素数として出力される
        
        FFT = FFT[0:freq_lines, :]                                # ナイキスト周波数以上のデータは削除する
        # FFT = np.abs(FFT)                                       # FFTの結果は複素数なので，絶対値にする
        FFT_real = np.real(FFT)                                   # FFTの実数部
        FFT_imag = np.imag(FFT)                                   # FFTの虚数部
        
        """ラベル作成"""      
        current_rate = time_series[time_end, 0]                   # FFTの最終時刻の終値
        future = time_end + predict_time_inc                      # FFTの最終時刻の直後      
        future_rate = time_series[future, 0]                      # FFTの最終時刻の直後の終値

        if current_rate >= future_rate:
            FFT_label = 0
#       elif current_rate == future_rate:
#           FFT_label = 2
        else:
            FFT_label = 1
        
        """FFT結果の結合"""
        FFT_complex = np.append(FFT_real, FFT_imag, axis=0)
        FFT_complex = FFT_complex.T
        # print("FFT_complex_shape:" + str(FFT_complex.shape))
        
        FFT_complex = FFT_complex.reshape(-1, 1)                                    # 縦ベクトルに変換する
        # print("FFT_complex_shape_1d:" + str(FFT_complex.shape))
        
        FFT_complex = np.append(FFT_complex, FFT_label)                             # ラベルの追記
        # print("FFT_complex_shape_label_add:" + str(FFT_complex.shape))                

        FFT_complex = FFT_complex.reshape(1, -1)                                    # 横ベクトルに変換する
        # print("FFT_complex_shape_label_add_transverse:" + str(FFT_complex.shape))
        
        spectrogram = np.append(spectrogram, FFT_complex, axis=0)                   # FFTの結果を追記する
        print("spectrogram_shape:" + str(spectrogram.shape))
                        
        time_start = time_start + time_inc                                          # FFTの開始時刻の更新
        time_end = int(time_start + num_sample)
        
        # print("num_series_data" + str(num_series_data))
        
    return spectrogram

def FFT_complex_2(file_name, num_sample, time_start = 0, over_lap = 1, predict_time_inc = 1):
    """終値を使用""" 
    # num_sample:時間窓長に含まれるサンプリング点数(2の乗数にすること)
    
    freq_lines = int(num_sample / 2)        # 分析ライン数 = サンプル数 / 2　https://www.onosokki.co.jp/HP-WK/c_support/faq/fft_common/fft_analys_4.htm
    spectrogram_idx = 2 * freq_lines + 1    # 2(実数，虚数) x 1(終値) = 2, 末尾の"+1"はラベル
    # print(freq_lines_idx)
    
    """為替時系列データの読込み"""
    print("reading the input file...")
    time_series = np.loadtxt(file_name, delimiter = ",", usecols = (5,), skiprows = 1)        # 終値，Volumeをnumpy配列として取得する
    num_series_data = time_series.shape[0]                                                    # データ数の取得 
    print("reading the input file is done")
    print("num_series_data")
    print(num_series_data)

    time_end = int(time_start + num_sample)
    time_inc = int(num_sample / over_lap)                         # FFT開始時刻の増分  

    spectrogram = np.empty((0, spectrogram_idx))

    while(time_end < (num_series_data - predict_time_inc)):
        
        print("time_start:" + str(time_start))
        
        """FFTの実行"""
        time_window = time_series[time_start:time_end]            # 時間窓長だけデータを抽出する
        
        """2次元配列に対してFFTを行う場合は，分析方向の指定が必要"""
        FFT = np.fft.fft(time_window, axis=0)                     # FFTの実行，結果は複素数として出力される
        
        FFT = FFT[0:freq_lines]                                   # ナイキスト周波数以上のデータは削除する
        # FFT = np.abs(FFT)                                       # FFTの結果は複素数なので，絶対値にする
        FFT_real = np.real(FFT)                                   # FFTの実数部
        FFT_imag = np.imag(FFT)                                   # FFTの虚数部
        
        """ラベル作成"""      
        current_rate = time_series[time_end]                      # FFTの最終時刻の終値
        future = time_end + predict_time_inc                      # FFTの最終時刻の直後      
        future_rate = time_series[future]                         # FFTの最終時刻の直後の終値

        if current_rate >= future_rate:
            FFT_label = 0
#       elif current_rate == future_rate:
#           FFT_label = 2
        else:
            FFT_label = 1
        
        """FFT結果の結合"""
        FFT_complex = np.append(FFT_real, FFT_imag, axis=0)
        FFT_complex = FFT_complex.T
        # print("FFT_complex_shape:" + str(FFT_complex.shape))
        
        FFT_complex = FFT_complex.reshape(-1, 1)                                    # 縦ベクトルに変換する
        # print("FFT_complex_shape_1d:" + str(FFT_complex.shape))
        
        FFT_complex = np.append(FFT_complex, FFT_label)                             # ラベルの追記
        # print("FFT_complex_shape_label_add:" + str(FFT_complex.shape))                

        FFT_complex = FFT_complex.reshape(1, -1)                                    # 横ベクトルに変換する
        # print("FFT_complex_shape_label_add_transverse:" + str(FFT_complex.shape))
        
        spectrogram = np.append(spectrogram, FFT_complex, axis=0)                   # FFTの結果を追記する
        print("spectrogram_shape:" + str(spectrogram.shape))
                        
        time_start = time_start + time_inc                                          # FFTの開始時刻の更新
        time_end = int(time_start + num_sample)
        
        # print("num_series_data" + str(num_series_data))
        
    return spectrogram
    
"""[def FFT_complex]を実行し，結果をcsvとして出力する関数============================================================================================="""

def carry_out_FFT_complex(input_file_name, output_file_name, num_sample, over_lap, ch_flag, predict_time_inc):
    # ch_flag = 2, 4, 6, 8, 10
    
    print("carry out FFT")
    
    if ch_flag == 10:
        spectrogram = FFT_complex_10(input_file_name, num_sample = num_sample, time_start = 0, over_lap = over_lap, predict_time_inc = predict_time_inc)
    elif ch_flag == 8:
        spectrogram = FFT_complex_8(input_file_name, num_sample = num_sample, time_start = 0, over_lap = over_lap, predict_time_inc = predict_time_inc)
    elif ch_flag == 6:
        spectrogram = FFT_complex_6(input_file_name, num_sample = num_sample, time_start = 0, over_lap = over_lap, predict_time_inc = predict_time_inc)
    elif ch_flag == 4:
        spectrogram = FFT_complex_4(input_file_name, num_sample = num_sample, time_start = 0, over_lap = over_lap, predict_time_inc = predict_time_inc)
    else:
        spectrogram = FFT_complex_2(input_file_name, num_sample = num_sample, time_start = 0, over_lap = over_lap, predict_time_inc = predict_time_inc)
    
    print("sound spectrogram")
    print(spectrogram)
    print("shape of spectrogram")
    print(spectrogram.shape)
    np.savetxt(output_file_name, spectrogram, delimiter = ",")
    print("end caluculation")

    return 0

"""[def FFT_complex]を実行し，結果をcsvとして出力する関数============================================================================================="""
# time_startを増加させて，FFTを連続実行する

def continuously_carry_out_FFT_complex(input_file_name, output_file_name, num_sample, over_lap, ch_flag, predict_time_inc):
    # ch_flag = 2, 4, 6, 8, 10
    
    print("carry out FFT")
    
    row_output_file_name = output_file_name
    
    for i in range(0, num_sample):
        output_file_name = str(i) + "_" + row_output_file_name
        print("time_start_inc:" + str(i))
        
        if ch_flag == 10:
            spectrogram = FFT_complex_10(input_file_name, num_sample = num_sample, time_start = i, over_lap = over_lap, predict_time_inc = predict_time_inc)
        elif ch_flag == 8:
            spectrogram = FFT_complex_8(input_file_name, num_sample = num_sample, time_start = i, over_lap = over_lap, predict_time_inc = predict_time_inc)
        elif ch_flag == 6:
            spectrogram = FFT_complex_6(input_file_name, num_sample = num_sample, time_start = i, over_lap = over_lap, predict_time_inc = predict_time_inc)
        elif ch_flag == 4:
            spectrogram = FFT_complex_4(input_file_name, num_sample = num_sample, time_start = i, over_lap = over_lap, predict_time_inc = predict_time_inc)
        else:
            spectrogram = FFT_complex_2(input_file_name, num_sample = num_sample, time_start = i, over_lap = over_lap, predict_time_inc = predict_time_inc)
        
        print("sound spectrogram")
        print(spectrogram)
        print("shape of spectrogram")
        print(spectrogram.shape)
        np.savetxt(output_file_name, spectrogram, delimiter = ",")
        print("end caluculation")

    return 0

"""始値，高値，安値，終値, VolumeのFFT結果から，実部，虚部のスペクトログラムを作成する関数============================================================"""

def create_spectrogram_10(temp_spectrogram, temp_all_data_size, input_dim=(10, 32, 32)):
    # input_dim = channel, height(time), width(freq)
    
    """スペクトログラムを作成するデータ範囲の初期化"""
    start_row = 0
    end_row = start_row + input_dim[1]
    start_col = 0
    end_col = start_col + input_dim[2]
    temp = np.empty((0, input_dim[0], input_dim[1], input_dim[2]))

    while(end_row <= temp_all_data_size):
        
        """データを抽出しスペクトログラムを作成する"""
        # 始値
        open_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        open_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        # 高値
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        high_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        high_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        # 安値
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        low_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        low_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        # 終値
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        close_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        close_imag = temp_spectrogram[start_row:end_row, start_col:end_col]

        # Volume
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        vol_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        vol_imag = temp_spectrogram[start_row:end_row, start_col:end_col]

        """スペクトログラムを作成するデータ範囲の更新"""
        # データを抽出する列の初期化
        start_col = 0
        end_col = start_col + input_dim[2]
        # データを抽出する行の更新
        start_row = start_row + input_dim[1]
        end_row = start_row + input_dim[1]

        """スペクトログラムの結合"""
        open_real = open_real.reshape(1, 1, input_dim[1], input_dim[2]) #(データ数, チャンネル, 高さ(time), 幅(freq))
        open_imag = open_imag.reshape(1, 1, input_dim[1], input_dim[2])
        high_real = high_real.reshape(1, 1, input_dim[1], input_dim[2])
        high_imag = high_imag.reshape(1, 1, input_dim[1], input_dim[2])
        low_real = low_real.reshape(1, 1, input_dim[1], input_dim[2])
        low_imag = low_imag.reshape(1, 1, input_dim[1], input_dim[2])
        close_real = close_real.reshape(1, 1, input_dim[1], input_dim[2])
        close_imag = close_imag.reshape(1, 1, input_dim[1], input_dim[2])
        vol_real = close_real.reshape(1, 1, input_dim[1], input_dim[2])
        vol_imag = close_imag.reshape(1, 1, input_dim[1], input_dim[2])
        
        open_real = np.append(open_real, open_imag, axis=1)
        open_real = np.append(open_real, high_real, axis=1)
        open_real = np.append(open_real, high_imag, axis=1)
        open_real = np.append(open_real, low_real, axis=1)
        open_real = np.append(open_real, low_imag, axis=1)
        open_real = np.append(open_real, close_real, axis=1)
        open_real = np.append(open_real, close_imag, axis=1)
        open_real = np.append(open_real, vol_real, axis=1)
        open_real = np.append(open_real, vol_imag, axis=1)
        
        temp = np.append(temp, open_real, axis=0)
        
    return temp
    
"""始値，高値，安値，終値のFFT結果から，実部，虚部のスペクトログラムを作成する関数===================================================================="""

def create_spectrogram_8(temp_spectrogram, temp_all_data_size, input_dim=(8, 32, 32)):
    # input_dim = channel, height(time), width(freq)
    
    """スペクトログラムを作成するデータ範囲の初期化"""
    start_row = 0
    end_row = start_row + input_dim[1]
    start_col = 0
    end_col = start_col + input_dim[2]
    temp = np.empty((0, input_dim[0], input_dim[1], input_dim[2]))

    while(end_row <= temp_all_data_size):
        
        """データを抽出しスペクトログラムを作成する"""
        # 始値
        open_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        open_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        # 高値
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        high_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        high_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        # 安値
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        low_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        low_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        # 終値
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        close_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        close_imag = temp_spectrogram[start_row:end_row, start_col:end_col]

        """スペクトログラムを作成するデータ範囲の更新"""
        # データを抽出する列の初期化
        start_col = 0
        end_col = start_col + input_dim[2]
        # データを抽出する行の更新
        start_row = start_row + input_dim[1]
        end_row = start_row + input_dim[1]

        """スペクトログラムの結合"""
        open_real = open_real.reshape(1, 1, input_dim[1], input_dim[2]) #(データ数, チャンネル, 高さ(time), 幅(freq))
        open_imag = open_imag.reshape(1, 1, input_dim[1], input_dim[2])
        high_real = high_real.reshape(1, 1, input_dim[1], input_dim[2])
        high_imag = high_imag.reshape(1, 1, input_dim[1], input_dim[2])
        low_real = low_real.reshape(1, 1, input_dim[1], input_dim[2])
        low_imag = low_imag.reshape(1, 1, input_dim[1], input_dim[2])
        close_real = close_real.reshape(1, 1, input_dim[1], input_dim[2])
        close_imag = close_imag.reshape(1, 1, input_dim[1], input_dim[2])
        
        open_real = np.append(open_real, open_imag, axis=1)
        open_real = np.append(open_real, high_real, axis=1)
        open_real = np.append(open_real, high_imag, axis=1)
        open_real = np.append(open_real, low_real, axis=1)
        open_real = np.append(open_real, low_imag, axis=1)
        open_real = np.append(open_real, close_real, axis=1)
        open_real = np.append(open_real, close_imag, axis=1)
        
        temp = np.append(temp, open_real, axis=0)
        
    return temp
    
"""高値，安値，終値のFFT結果から，実部，虚部のスペクトログラムを作成する関数=========================================================================="""

def create_spectrogram_6(temp_spectrogram, temp_all_data_size, input_dim=(6, 32, 32)):
    # input_dim = channel, height(time), width(freq)
    
    """スペクトログラムを作成するデータ範囲の初期化"""
    start_row = 0
    end_row = start_row + input_dim[1]
    start_col = 0
    end_col = start_col + input_dim[2]
    temp = np.empty((0, input_dim[0], input_dim[1], input_dim[2]))

    while(end_row <= temp_all_data_size):
        
        """データを抽出しスペクトログラムを作成する"""
        # 高値
        high_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        high_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        # 安値
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        low_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        low_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        # 終値
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        close_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        close_imag = temp_spectrogram[start_row:end_row, start_col:end_col]

        """スペクトログラムを作成するデータ範囲の更新"""
        # データを抽出する列の初期化
        start_col = 0
        end_col = start_col + input_dim[2]
        # データを抽出する行の更新
        start_row = start_row + input_dim[1]
        end_row = start_row + input_dim[1]

        """スペクトログラムの結合"""
        high_real = high_real.reshape(1, 1, input_dim[1], input_dim[2]) #(データ数, チャンネル, 高さ(time), 幅(freq))
        high_imag = high_imag.reshape(1, 1, input_dim[1], input_dim[2])
        low_real = low_real.reshape(1, 1, input_dim[1], input_dim[2])
        low_imag = low_imag.reshape(1, 1, input_dim[1], input_dim[2])
        close_real = close_real.reshape(1, 1, input_dim[1], input_dim[2])
        close_imag = close_imag.reshape(1, 1, input_dim[1], input_dim[2])
        
        high_real = np.append(high_real, high_imag, axis=1)
        high_real = np.append(high_real, low_real, axis=1)
        high_real = np.append(high_real, low_imag, axis=1)
        high_real = np.append(high_real, close_real, axis=1)
        high_real = np.append(high_real, close_imag, axis=1)
        
        temp = np.append(temp, high_real, axis=0)
        
    return temp

"""終値,VolumeのFFT結果から，実部，虚部のスペクトログラムを作成する関数==============================================================================="""

def create_spectrogram_4(temp_spectrogram, temp_all_data_size, input_dim=(4, 32, 32)):
    # input_dim = channel, height(time), width(freq)
    
    """スペクトログラムを作成するデータ範囲の初期化"""
    start_row = 0
    end_row = start_row + input_dim[1]
    start_col = 0
    end_col = start_col + input_dim[2]
    temp = np.empty((0, input_dim[0], input_dim[1], input_dim[2]))

    while(end_row <= temp_all_data_size):
        
        """データを抽出しスペクトログラムを作成する"""
        # 終値
        close_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        close_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        # Volume
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        vol_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        vol_imag = temp_spectrogram[start_row:end_row, start_col:end_col]

        """スペクトログラムを作成するデータ範囲の更新"""
        # データを抽出する列の初期化
        start_col = 0
        end_col = start_col + input_dim[2]
        # データを抽出する行の更新
        start_row = start_row + input_dim[1]
        end_row = start_row + input_dim[1]

        """スペクトログラムの結合"""
        close_real = close_real.reshape(1, 1, input_dim[1], input_dim[2]) #(データ数, チャンネル, 高さ(time), 幅(freq))
        close_imag = close_imag.reshape(1, 1, input_dim[1], input_dim[2])
        vol_real = vol_real.reshape(1, 1, input_dim[1], input_dim[2])
        vol_imag = vol_imag.reshape(1, 1, input_dim[1], input_dim[2])
        
        close_real = np.append(close_real, close_imag, axis=1)
        close_real = np.append(close_real, vol_real, axis=1)
        close_real = np.append(close_real, vol_imag, axis=1)
        
        temp = np.append(temp, close_real, axis=0)
        
    return temp

"""終値のFFT結果から，実部，虚部のスペクトログラムを作成する関数======================================================================================"""
    
def create_spectrogram_2(temp_spectrogram, temp_all_data_size, input_dim=(2, 32, 32)):
    # input_dim = channel, height(time), width(freq)
    
    """スペクトログラムを作成するデータ範囲の初期化"""
    start_row = 0
    end_row = start_row + input_dim[1]
    start_col = 0
    end_col = start_col + input_dim[2]
    temp = np.empty((0, input_dim[0], input_dim[1], input_dim[2]))

    while(end_row <= temp_all_data_size):
        
        """データを抽出しスペクトログラムを作成する"""
        # 終値
        close_real = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        start_col = start_col + input_dim[2]
        end_col = start_col + input_dim[2]
        close_imag = temp_spectrogram[start_row:end_row, start_col:end_col]
        
        """スペクトログラムを作成するデータ範囲の更新"""
        # データを抽出する列の初期化
        start_col = 0
        end_col = start_col + input_dim[2]
        # データを抽出する行の更新
        start_row = start_row + input_dim[1]
        end_row = start_row + input_dim[1]

        """スペクトログラムの結合"""
        close_real = close_real.reshape(1, 1, input_dim[1], input_dim[2]) #(データ数, チャンネル, 高さ(time), 幅(freq))
        close_imag = close_imag.reshape(1, 1, input_dim[1], input_dim[2])
        
        close_real = np.append(close_real, close_imag, axis=1)
        
        temp = np.append(temp, close_real, axis=0)
        
    return temp
    
"""スペクトログラムを作成し，１つのnumpy配列にまとめる関数============================================================================================"""

def merge_spectrogram(spectrogram_file_name, input_dim, ch_flag):
    # input_dim = channel, height(time), width(freq)
    
    col = int(input_dim[0] * input_dim[2])
    input_data_col = range(col)             # FFTの結果が保存されている列を指定
    label_col = (col,)                      # ラベルが保存されている列を指定
    col = input_dim[1] - 1
    
    # 全てのFFT結果とラベルを格納する
    raw_spectrogram = open_input_for_spectrogram(spectrogram_file_name, input_data_col)  # 為替時系列データのFFT結果を読込む
    raw_spectrogram_label = open_label_for_spectrogram(spectrogram_file_name, label_col) # ラベルを読込む
    print("raw_FFT_shape:" + str(raw_spectrogram.shape))
    print("raw_FFT_label_shape:" + str(raw_spectrogram_label.shape))
    
    # 全てのデータを格納する配列
    spectrogram = np.empty((0, input_dim[0], input_dim[1], input_dim[2]))
    spectrogram_label = np.empty(0, dtype=int)
    
    # オーバーラップさせながらスペクトログラムを作成する
    over_lap_inc = 1
    over_lap_start = 0
    over_lap_end = int((input_dim[1] - 1) / over_lap_inc) * over_lap_inc + 1

    for i in range(over_lap_start, over_lap_end, over_lap_inc):
        print("over_lap_num:" + str(i))
        
        temp_spectrogram = raw_spectrogram[i:]
        temp_spectrogram_label = raw_spectrogram_label[i:]
        temp_all_data_size = temp_spectrogram.shape[0]
        
        # ラベルを抽出する際に，all_data_sizeはインプットデータの高さで割り切れなければならないため，ここでスライスする
        temp_all_data_size = int(temp_all_data_size / input_dim[1]) * input_dim[1]
        temp_spectrogram = temp_spectrogram[0:temp_all_data_size]
        temp_spectrogram_label = temp_spectrogram_label[0:temp_all_data_size]

        # スペクトログラムの作成
        if ch_flag == 10:
            temp_spectrogram = create_spectrogram_10(temp_spectrogram, temp_all_data_size, input_dim)
        elif ch_flag == 8:
            temp_spectrogram = create_spectrogram_8(temp_spectrogram, temp_all_data_size, input_dim)
        elif ch_flag == 6:
            temp_spectrogram = create_spectrogram_6(temp_spectrogram, temp_all_data_size, input_dim)
        elif ch_flag == 4:
            temp_spectrogram = create_spectrogram_4(temp_spectrogram, temp_all_data_size, input_dim)
        else:
            temp_spectrogram = create_spectrogram_2(temp_spectrogram, temp_all_data_size, input_dim)
        
        temp_all_data_size = temp_spectrogram.shape[0]  # temp_all_data_sizeを更新
        
        # 各スペクトログラムのラベルを抽出する．各スペクトログラムのラベル = 各データの最終FFTのラベル
        temp_spectrogram_label = temp_spectrogram_label.reshape(temp_all_data_size, input_dim[1])
        temp_spectrogram_label = temp_spectrogram_label[:, col]

        # print("temp_spectrogram_shape:" + str(temp_spectrogram.shape))
        # print("temp_spectrogram_label_shape" + str(temp_spectrogram_label.shape))  

        # 全てのデータはspectrogramとspectrogram_labelに格納される   
        spectrogram = np.append(spectrogram, temp_spectrogram, axis=0)
        spectrogram_label = np.append(spectrogram_label, temp_spectrogram_label, axis=0)

        print("spectrogram_shape:" + str(spectrogram.shape))
        print("spectrogram_label_shape:" + str(spectrogram_label.shape))
        
    return spectrogram, spectrogram_label