In [1]:

    

import pandas as pd
import numpy as np
%matplotlib inline


    

In [2]:

    

train = np.load("../train_data/noised.npy")
train


    

    
Out[2]:





array([[ 0.02579258,  0.11744915],
       [ 0.11744915,  0.22526896],
       [ 0.22526896,  0.36917262],
       ..., 
       [-0.32294043, -0.20061926],
       [-0.20061926, -0.14804184],
       [-0.14804184, -0.01545028]])

In [3]:

    

initial = np.load("initial.npy")
initial


    

    
Out[3]:





array([ 0.02579258,  0.11744915,  0.22526896,  0.36917262,  0.47340135,
        0.61050527,  0.67007774,  0.76901937,  0.84876636,  0.92144541,
        0.95120124,  0.97796587,  0.99930625,  0.99721358,  0.9866561 ,
        0.93759994,  0.88322474,  0.82730208,  0.74426211,  0.69826256,
        0.56903788,  0.44642936,  0.35095682,  0.25138713,  0.16483783,
        0.00658277, -0.13632656, -0.28847   , -0.4110921 , -0.47973775,
       -0.61694071, -0.66689308, -0.78959501, -0.84692734, -0.88307591,
       -0.95761569, -0.98034115, -0.99953955, -0.99682984, -0.99040903,
       -0.95125477, -0.88856753, -0.85777152, -0.78153686, -0.65707738,
       -0.61249401, -0.47582673, -0.39229891, -0.20314946, -0.09520136])

In [4]:

    

output = np.load("output.npy")
output


    

    
Out[4]:





array([ 0.07019901,  0.23288783,  0.38954934,  0.52992344,  0.64635241,
        0.73576295,  0.79919326,  0.8400166 ,  0.86222863,  0.86943018,
        0.86444163,  0.84927106,  0.82521725,  0.79299629,  0.75284815,
        0.70461655,  0.64781046,  0.58166265,  0.505211  ,  0.41743484,
        0.31749383,  0.20511317,  0.08112517, -0.05193378, -0.18945259,
       -0.32501662, -0.45155513, -0.56301236, -0.6556747 , -0.72848582,
       -0.78244221, -0.81965375, -0.84255373, -0.85341978, -0.85415387,
       -0.84622133, -0.83066511, -0.80814874, -0.77900243, -0.74326217,
       -0.70070171, -0.65086198, -0.59308541, -0.52657205, -0.45047924,
       -0.3640945 , -0.267111  , -0.1600136 , -0.04452255,  0.07606298,
        0.19687682,  0.31207076,  0.41591802,  0.5039978 ,  0.57389879,
        0.62518728,  0.65883756,  0.67651546,  0.6800015 ,  0.67084062,
        0.65018368,  0.61874712,  0.57683742,  0.52441001,  0.46115842,
        0.38664541,  0.30050316,  0.20272815,  0.09407678, -0.02349532,
       -0.14651513, -0.27005124, -0.38834971, -0.49594745, -0.5887897 ,
       -0.66481465, -0.72383118, -0.76694286, -0.79589343, -0.81256473,
       -0.81867814, -0.81565475, -0.80457318, -0.78617597, -0.76089549,
       -0.72888291, -0.69003701, -0.64403164, -0.59034938, -0.52833235,
       -0.45726755, -0.37653255, -0.28582314, -0.1854755 , -0.07684691,
        0.03735572,  0.15299901,  0.26489687,  0.3676627 ,  0.45675907])

In [5]:

    

train_df = pd.DataFrame(train[:len(initial) + len(output), 0], columns=["train"])
initial_df = pd.DataFrame(initial, columns=["initial"])
output_df = pd.DataFrame(output, columns=["output"], index=range(len(initial), len(initial) + len(output)))
merged = pd.concat([train_df, initial_df, output_df])
merged.plot(style=["-", "-", "k--"], figsize=(15, 5), grid=True)


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x1126e6710>






    

In [ ]: