# Implementations partly based on code from
# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
# and https://gist.github.com/danijar/61f9226f7ea498abce36187ddaf51ed5

import tensorflow as tf

import random
import numpy
import csv
from matplotlib import pyplot as plt
from collections import defaultdict

# Read in propofol data

f = open("merged.csv", 'r') # Includes extra features
r = csv.reader(f)
header = next(r)
print(header) # Show what features are included

['ID', 'AMT', 'RATE', 'PUMP', 'BLOC', 'CLOC', 'INSULIN', 'TLOC', 'MDV', 'IPRED', 'IWRES', 'V1', 'V2', 'V3', 'V4', 'CL1', 'CL2', 'CL3', 'CL4', 'CONC', 'BIS', 'TBW', 'LBW', 'CO', 'AGE', 'SEXM', 'SEXF', 'CONC.1', 'PRED', 'RES', 'WRES', 'TIME']

# Extract maxima from each feature, to be used later for feature scaling
r1 = csv.reader(open("merged.csv", 'r'))
header = next(r1)
maxima = defaultdict(float)
for row in r1:
    d = dict(zip(header, row))
    for h in header:
        try:
            fl = float(d[h])
            if fl > maxima[h]:
                maxima[h] = fl
        except Exception as e:
            pass

maxima

defaultdict(float,
            {'AGE': 69.0,
             'AMT': 330.0,
             'BIS': 98.0,
             'BLOC': 97.0,
             'CL1': 36.365,
             'CL2': 1.8885,
             'CL3': 30.51,
             'CL4': 0.35779,
             'CLOC': 4811.65,
             'CO': 17.59,
             'CONC': 51.183,
             'CONC.1': 51.183,
             'ID': 30.0,
             'INSULIN': 47.0,
             'IPRED': 34.023,
             'IWRES': 2.5163,
             'LBW': 96.8,
             'MDV': 1.0,
             'PRED': 22.054,
             'PUMP': 161.3,
             'RATE': 151.7,
             'RES': 39.486,
             'SEXF': 1.0,
             'SEXM': 1.0,
             'TBW': 176.6,
             'TIME': 1041.95,
             'TLOC': 2.58,
             'V1': 3.3729,
             'V2': 7.5302,
             'V3': 352.3,
             'V4': 23.042,
             'WRES': 16.721})

# Read the data and perform scaling on the features
def readRows(r):
    prevID = None
    seqX, seqXMEM, seqY, seqMEM, timeVec = [], [], [], [], []
    for row in r:
        d = dict(zip(header, row))
        if len(seqX) and d['ID'] != prevID:
            yield seqX, seqXMEM, seqY, seqMEM, timeVec, prevID
            seqX, seqXMEM, seqY, seqMEM, timeVec = [], [], [], [], []
        prevID = d['ID']
        d['CONST'] = 1 # Constant feature (offset/bias term)
        maxima['CONST'] = 1
        feat = [float(d[f])/maxima[f] for f in ['TIME', 'CONST',
                                                'TBW', 'LBW', 'CO', 
                                                'AGE', 'SEXM', 'SEXF']]
        feat += [len(seqX), float(d['TIME']) < 10]
        featMEM = [float(d[f])/maxima[f] for f in ['V1', 'V2', 'V3', 'V4',
                                                   'CL1', 'CL2', 'CL3', 'CL4']]
        featMEM += [float(d['PRED'])]
        featAll = feat + featMEM
        y = float(d['CONC'])
        seqX.append(feat)
        seqXMEM.append(featAll)
        seqY.append(y)
        seqMEM.append(float(d['PRED']))
        timeVec.append(float(d['TIME']))

# Build feature matrices for the various models
XX = []
XXMEM = []
yy = []
yyMEM = []
timeVecs = []
ids = []

for X, XMEM, y, yMEM, timeVec, pid in readRows(r):
    XX.append(X)
    XXMEM.append(XMEM)
    yy.append(y)
    yyMEM.append(yMEM)
    timeVecs.append(timeVec)
    ids.append(pid)

nObs = 47

ids = [ids[i] for i in range(len(ids)) if len(XX[i]) == nObs] # Make sure all instances have the same length
XX = [X for X in XX if len(X) == nObs] # Make sure all instances have the same length
XXMEM = [X for X in XXMEM if len(X) == nObs]
yy = [y for y in yy if len(y) == nObs]
yyMEM = [y for y in yyMEM if len(y) == nObs]
timeVecs = [t for t in timeVecs if len(t) == nObs]

n_test = 2
N = len(XX)

XX = numpy.array(XX, dtype='float32')
XXMEM = numpy.array(XXMEM, dtype='float32')
yy = numpy.array(yy, dtype='float32')
yyMEM = numpy.array(yyMEM, dtype='float32')
timeVecs = numpy.array(timeVecs, dtype='float32')

# Tensorflow GRU setup

num_units = 10
num_layers = 2

print(XX.shape)
print(yy.shape)

(22, 47, 10)
(22, 47)

# GRU cell with dropout
def cellGen(XX):
    dropout = 0.05 # Dropout regularization
    cells = []
    for _ in range(num_layers):
        cell = tf.contrib.rnn.GRUCell(num_units) # Or LSTMCell(num_units)
        cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=1.0-dropout)
        cells.append(cell)
    cell = tf.contrib.rnn.MultiRNNCell(cells)
    output, _ = tf.nn.dynamic_rnn(cell, XX, dtype=tf.float32)
    return output

# Makes a prediction given feature matrix XX as input. Uses the GRU cell (above) and performs simple ensembling
def prediction(XX):
    output = cellGen(XX)
    output2 = tf.constant(XX)
    out_size = 1 # Regression task, rather than multiclass
    logit = tf.contrib.layers.fully_connected(output, out_size)
    init = [0] * XX.shape[2]
    init[1] = 1
    h2 = tf.Variable(numpy.array([init], dtype="float32").T)
    logit2 = tf.einsum('ijk,kl->ijl', output2, h2)
    h3 = tf.Variable(numpy.array([init], dtype="float32").T)
    logit3 = tf.einsum('ijk,kl->ijl', output2, h3)
    return tf.math.softplus(logit + logit2) + tf.nn.sigmoid(logit3)*tf.reshape(XX[:,:,-1],logit3.shape)

# Defines the objective to be optimized
def loss(XX, yy, n_valid = 0):
    logit = prediction(XX)
    flat_logit = tf.reshape(logit, [-1])
    flat_target = tf.reshape(yy, [-1])
    l = flat_target/flat_logit - 1
    loss = tf.reduce_mean(tf.abs(l[:-n_test*nObs]))
    validError = tf.reduce_mean(tf.abs(l[-n_test*nObs:-(n_test-n_valid)*nObs]))
    testError = tf.reduce_mean(tf.abs(l[-(n_test-n_valid)*nObs:]))
    return loss, validError, testError, logit

# Calculates the error (used to report performance)
def PE(preds, labels):
    ps = numpy.reshape(preds, [-1])
    ls = numpy.reshape(labels, [-1])
    error = numpy.median(numpy.abs(numpy.nan_to_num((ls - ps)/ps)))
    bias = numpy.abs(numpy.median(numpy.abs(numpy.nan_to_num((ls - ps)/ps))))
    return error

# Training loop (no cross-validation yet, just a fixed test/train set)
def tfTrain(XX, yy, iterations = 10000, progress = True, n_valid = 0):
    obj, validError, testError, preds = loss(XX, yy, n_valid)
    optimizer = tf.train.AdamOptimizer(0.0001)
    train = optimizer.minimize(obj)
    
    init = tf.global_variables_initializer()
    sess = tf.Session()
    sess.run(init)
    
    bestTrain = None
    bestValid = None
    predictions = None
    te = None
    sinceBetter = 0
    iteration = 0
    while (sinceBetter < 1000): # Terminate if no better performance (on validation set) for 1000 iterations
        iteration += 1
        cvalues = sess.run([train, obj, validError, testError, preds])#, preds])
        trainObj = cvalues[1]
        p2 = numpy.squeeze(numpy.array(cvalues[4]))
        p2train = p2[:-n_test] # Split the data into a training set, validation set, and test set
        p2valid = p2[-n_test:-(n_test-n_valid)]
        p2test = p2[-(n_test-n_valid):]
        yytrain = yy[:-n_test]
        yyvalid = yy[-n_test:-(n_test-n_valid)]
        yytest = yy[-(n_test-n_valid):]
        realTrain = PE(p2train, yytrain)
        realValid = PE(p2valid, yyvalid)
        realTest = PE(p2test, yytest)
        if (iteration % 1000) == 0:
            print("  Iteration " + str(iteration) +
                  ": obj = " + str(trainObj) +
                  ", train = " + str(realTrain) + 
                  ", valid = " + str(realValid) +
                  ", test = " + str(realTest))
        if n_valid: # If a validation set is being used, select the best model based on validation performance
            if (bestValid == None or realValid < bestValid):
                te = realTest
                predictions = cvalues[4]
                bestValid = realValid
                print("  Iteration " + str(iteration) +
                      ": obj = " + str(trainObj) +
                      ", train = " + str(realTrain) + 
                      ", valid = " + str(realValid) +
                      ", test = " + str(realTest))
                sinceBetter = 0
        elif bestTrain == None or realTrain < bestTrain: # Otherwise select the best model based on training perf.
            te = realTest
            predictions = cvalues[4]
            bestTrain = realTrain
            print("  Iteration " + str(iteration) +
                  ": obj = " + str(trainObj) +
                  ", train = " + str(realTrain) + 
                  ", valid = " + str(realValid) +
                  ", test = " + str(realTest))
            sinceBetter = 0
        sinceBetter += 1
    return predictions, te

errors = []
n_test = 4 # Each cross-validation trial withholds 4 out of 22 datapoints. 2 are used for validation and 2 for test.
n_valid = 2
better = 0 # How many of the cross-validation trials worked better for the ANN?

XXcopy, XXMEMcopy = list(XX), list(XXMEM)
yycopy, yyMEMcopy = list(yy), list(yyMEM)
combined = list(zip(XXcopy, XXMEMcopy, yycopy, yyMEMcopy))

predictionVec = []

for trial in range(11): # 11 Rounds of cross validation for 22 samples
    tf.reset_default_graph()
    tf.set_random_seed(0)
    XXcopy, XXMEMcopy = [x[0] for x in combined], [x[1] for x in combined]
    yycopy, yyMEMcopy = [x[2] for x in combined], [x[3] for x in combined]
    XXcopy, XXMEMcopy = numpy.array(XXcopy), numpy.array(XXMEMcopy)
    yycopy, yyMEMcopy = numpy.array(yycopy), numpy.array(yyMEMcopy)
    predictionsMEM, testErrorMEM = tfTrain(XXMEMcopy, yycopy, n_valid = n_valid)
    # Ugly, but basically just builds the prediction vector by concatenating the results from this trial
    predictionVec = list(predictionsMEM[-(n_test - n_valid):]) + predictionVec
    print(numpy.array(predictionVec).shape)
    errorMEM = PE(yyMEMcopy[-(n_test - n_valid):], yycopy[-(n_test - n_valid):])
    errorGRU = PE(predictionsMEM[-(n_test - n_valid):], yycopy[-(n_test - n_valid):])
    if testErrorMEM < errorMEM:
        better += 1
    print("MEM error = " + str(errorMEM)[:5] +
          ", GRU error = " + str(testErrorMEM)[:5] + '/' + str(errorGRU) +
          ", better = " + str(better) + '/' + str(trial + 1))
    errors.append([errorMEM, testErrorMEM])
    print("overall MEM error   = " + str(sum([x[0] for x in errors]) / len(errors))[:5])
    print("overall GRU error = " + str(sum([x[1] for x in errors]) / len(errors))[:5])
    combined = combined[-n_valid:] + combined[:-n_valid]

predictionVec = numpy.squeeze(numpy.array(predictionVec))

  Iteration 1: obj = 0.75876504, train = 0.8475504, valid = 0.8494255, test = 0.68609875
  Iteration 2: obj = 0.7584884, train = 0.84695005, valid = 0.848544, test = 0.68436885
  Iteration 3: obj = 0.7582058, train = 0.846883, valid = 0.8476651, test = 0.6833785
  Iteration 4: obj = 0.7579849, train = 0.8464785, valid = 0.8467889, test = 0.6824023
  Iteration 5: obj = 0.7576203, train = 0.84585977, valid = 0.84652317, test = 0.6814283
  Iteration 6: obj = 0.75742984, train = 0.8457765, valid = 0.8462923, test = 0.68045664
  Iteration 7: obj = 0.75720876, train = 0.84569097, valid = 0.84605724, test = 0.67948544
  Iteration 8: obj = 0.7567571, train = 0.8456049, valid = 0.84582096, test = 0.6785165
  Iteration 9: obj = 0.75680995, train = 0.84551847, valid = 0.84558356, test = 0.6775496
  Iteration 10: obj = 0.7565637, train = 0.8454311, valid = 0.84534425, test = 0.6765846
  Iteration 11: obj = 0.75619364, train = 0.8453425, valid = 0.84510267, test = 0.67562103
  Iteration 12: obj = 0.7558802, train = 0.84525263, valid = 0.8448585, test = 0.674659
  Iteration 13: obj = 0.7556067, train = 0.84470654, valid = 0.8446115, test = 0.6736983
  Iteration 14: obj = 0.75534374, train = 0.84264207, valid = 0.84436125, test = 0.6727389
  Iteration 15: obj = 0.7552431, train = 0.8436017, valid = 0.8441073, test = 0.67178035
  Iteration 16: obj = 0.75507796, train = 0.84220797, valid = 0.84384924, test = 0.6708226
  Iteration 17: obj = 0.754711, train = 0.8432938, valid = 0.8435868, test = 0.6670998
  Iteration 18: obj = 0.7546068, train = 0.8425391, valid = 0.8433198, test = 0.6689092
  Iteration 19: obj = 0.7542568, train = 0.8420491, valid = 0.84304404, test = 0.6679517
  Iteration 20: obj = 0.7540787, train = 0.8419878, valid = 0.8427601, test = 0.6669936
  Iteration 21: obj = 0.7539388, train = 0.8419294, valid = 0.842481, test = 0.66604114
  Iteration 22: obj = 0.7535504, train = 0.84187114, valid = 0.84240544, test = 0.6650922
  Iteration 25: obj = 0.75285053, train = 0.83979964, valid = 0.8422259, test = 0.66226846
  Iteration 26: obj = 0.7525388, train = 0.83975935, valid = 0.84216464, test = 0.66133326
  Iteration 27: obj = 0.75239754, train = 0.83992755, valid = 0.84210217, test = 0.66040033
  Iteration 28: obj = 0.7523288, train = 0.8385414, valid = 0.84203804, test = 0.6594692
  Iteration 30: obj = 0.75173235, train = 0.83974725, valid = 0.8419032, test = 0.6576104
  Iteration 32: obj = 0.7513009, train = 0.8375689, valid = 0.84175867, test = 0.65575516
  Iteration 33: obj = 0.7510148, train = 0.83764255, valid = 0.84168243, test = 0.6549393
  Iteration 34: obj = 0.75063586, train = 0.8376545, valid = 0.84160256, test = 0.6544744
  Iteration 35: obj = 0.75048625, train = 0.837597, valid = 0.84151995, test = 0.65400076
  Iteration 36: obj = 0.75035584, train = 0.8375981, valid = 0.8414348, test = 0.65351856
  Iteration 38: obj = 0.749765, train = 0.8374103, valid = 0.841256, test = 0.65252733
  Iteration 39: obj = 0.7495537, train = 0.8373432, valid = 0.8411627, test = 0.65201885
  Iteration 40: obj = 0.7491815, train = 0.83727324, valid = 0.841066, test = 0.6514999
  Iteration 41: obj = 0.74903345, train = 0.8372005, valid = 0.84096646, test = 0.65097153
  Iteration 42: obj = 0.7489173, train = 0.8371253, valid = 0.8408641, test = 0.65043473
  Iteration 43: obj = 0.7483782, train = 0.8370476, valid = 0.84075904, test = 0.64988923
  Iteration 44: obj = 0.7485219, train = 0.8369679, valid = 0.84065163, test = 0.6493368
  Iteration 46: obj = 0.7477715, train = 0.83666784, valid = 0.8404302, test = 0.6482121
  Iteration 47: obj = 0.74760103, train = 0.83669525, valid = 0.8403164, test = 0.6476407
  Iteration 48: obj = 0.74772894, train = 0.83588624, valid = 0.84029883, test = 0.6470631
  Iteration 49: obj = 0.7471684, train = 0.835083, valid = 0.8400824, test = 0.6464791
  Iteration 51: obj = 0.7466774, train = 0.8329317, valid = 0.84007245, test = 0.645292
  Iteration 52: obj = 0.7468727, train = 0.8325404, valid = 0.8398677, test = 0.64468956
  Iteration 54: obj = 0.7460108, train = 0.831771, valid = 0.83974, test = 0.64346766
  Iteration 55: obj = 0.7459615, train = 0.82879436, valid = 0.8393293, test = 0.6428488
  Iteration 59: obj = 0.7452053, train = 0.8298867, valid = 0.8390781, test = 0.6403799
  Iteration 62: obj = 0.74409354, train = 0.828881, valid = 0.83896005, test = 0.63851506
  Iteration 63: obj = 0.7437446, train = 0.8286134, valid = 0.8388766, test = 0.634729
  Iteration 65: obj = 0.74325144, train = 0.8280878, valid = 0.8386774, test = 0.63662606
  Iteration 66: obj = 0.7429312, train = 0.8278297, valid = 0.8378537, test = 0.6359694
  Iteration 72: obj = 0.7411095, train = 0.8254672, valid = 0.8373575, test = 0.62827003
  Iteration 78: obj = 0.7399621, train = 0.82332015, valid = 0.83731407, test = 0.6187698
  Iteration 79: obj = 0.7398875, train = 0.82338357, valid = 0.8371298, test = 0.62197745
  Iteration 80: obj = 0.7398771, train = 0.8233824, valid = 0.8370398, test = 0.60065496
  Iteration 82: obj = 0.73928356, train = 0.82251227, valid = 0.8367474, test = 0.6227529
  Iteration 84: obj = 0.7387864, train = 0.8217864, valid = 0.83634806, test = 0.6144799
  Iteration 85: obj = 0.73903173, train = 0.8216378, valid = 0.8363173, test = 0.60814744
  Iteration 88: obj = 0.7375613, train = 0.8207663, valid = 0.8355439, test = 0.61055946
  Iteration 92: obj = 0.7368179, train = 0.8196142, valid = 0.8353514, test = 0.60465115
  Iteration 93: obj = 0.7363769, train = 0.8192096, valid = 0.83534753, test = 0.60367894
  Iteration 95: obj = 0.7356893, train = 0.81764865, valid = 0.83520395, test = 0.5961579
  Iteration 96: obj = 0.73563415, train = 0.81800866, valid = 0.8350805, test = 0.60690147
  Iteration 97: obj = 0.73516506, train = 0.81827223, valid = 0.8348886, test = 0.5967881
  Iteration 98: obj = 0.734435, train = 0.8172146, valid = 0.83364946, test = 0.5991657
  Iteration 100: obj = 0.73429257, train = 0.8156841, valid = 0.8333778, test = 0.5956063
  Iteration 107: obj = 0.73244643, train = 0.8128133, valid = 0.8333564, test = 0.58591247
  Iteration 108: obj = 0.7317347, train = 0.8142227, valid = 0.8329604, test = 0.57438517
  Iteration 109: obj = 0.73201644, train = 0.81116045, valid = 0.8321438, test = 0.58365583
  Iteration 113: obj = 0.7314802, train = 0.81416285, valid = 0.8298665, test = 0.578049
  Iteration 126: obj = 0.7291887, train = 0.8101821, valid = 0.8294421, test = 0.5701467
  Iteration 131: obj = 0.7272029, train = 0.8096035, valid = 0.82916814, test = 0.566855
  Iteration 134: obj = 0.72664946, train = 0.7990361, valid = 0.82884973, test = 0.57101953
  Iteration 137: obj = 0.7256162, train = 0.7946068, valid = 0.828641, test = 0.56083494
  Iteration 138: obj = 0.7253922, train = 0.7954625, valid = 0.82500553, test = 0.54644835
  Iteration 148: obj = 0.723225, train = 0.78837186, valid = 0.822678, test = 0.54610246
  Iteration 158: obj = 0.72104627, train = 0.79283965, valid = 0.8201691, test = 0.53348255
  Iteration 171: obj = 0.7185324, train = 0.7877018, valid = 0.8168055, test = 0.5229875
  Iteration 183: obj = 0.7176764, train = 0.78227353, valid = 0.81640905, test = 0.51782626
  Iteration 184: obj = 0.7169789, train = 0.78780735, valid = 0.816213, test = 0.54664505
  Iteration 185: obj = 0.7156235, train = 0.7841803, valid = 0.81508875, test = 0.51354074
  Iteration 189: obj = 0.7163537, train = 0.7801604, valid = 0.8148138, test = 0.5086342
  Iteration 190: obj = 0.71555865, train = 0.7852267, valid = 0.81091, test = 0.50835454
  Iteration 195: obj = 0.7152768, train = 0.78423536, valid = 0.81033045, test = 0.5170462
  Iteration 196: obj = 0.71341455, train = 0.7852087, valid = 0.8095701, test = 0.52261555
  Iteration 199: obj = 0.7146647, train = 0.7832968, valid = 0.8072673, test = 0.5065629
  Iteration 200: obj = 0.71362966, train = 0.7862114, valid = 0.80648744, test = 0.50644064
  Iteration 201: obj = 0.7119046, train = 0.77877766, valid = 0.8056989, test = 0.5217606
  Iteration 202: obj = 0.7138966, train = 0.7798234, valid = 0.8049059, test = 0.5213944
  Iteration 205: obj = 0.7122063, train = 0.7791522, valid = 0.8025061, test = 0.51028574
  Iteration 206: obj = 0.7126351, train = 0.78590536, valid = 0.8016939, test = 0.5100077
  Iteration 207: obj = 0.7114244, train = 0.77993417, valid = 0.8008733, test = 0.5029328
  Iteration 209: obj = 0.71043473, train = 0.77807206, valid = 0.7992204, test = 0.5098187
  Iteration 210: obj = 0.7124503, train = 0.78282887, valid = 0.79838574, test = 0.50105286
  Iteration 212: obj = 0.71070707, train = 0.77875316, valid = 0.79670155, test = 0.50700897
  Iteration 213: obj = 0.71174145, train = 0.77983725, valid = 0.79586, test = 0.49827468
  Iteration 214: obj = 0.7111869, train = 0.7806999, valid = 0.7950152, test = 0.5018194
  Iteration 216: obj = 0.71092355, train = 0.7798892, valid = 0.7933042, test = 0.48210436
  Iteration 218: obj = 0.7087511, train = 0.7779182, valid = 0.7915671, test = 0.49358237
  Iteration 219: obj = 0.71051437, train = 0.77809393, valid = 0.7906863, test = 0.48894992
  Iteration 221: obj = 0.7075265, train = 0.77620184, valid = 0.788908, test = 0.49429426
  Iteration 222: obj = 0.7076299, train = 0.77650034, valid = 0.78801715, test = 0.49142128
  Iteration 223: obj = 0.70806515, train = 0.77637595, valid = 0.7871155, test = 0.48684642
  Iteration 224: obj = 0.7090772, train = 0.7766355, valid = 0.7862098, test = 0.49994504
  Iteration 225: obj = 0.70848715, train = 0.77599764, valid = 0.7852958, test = 0.4767393
  Iteration 226: obj = 0.7071214, train = 0.7730537, valid = 0.78438103, test = 0.47669956
  Iteration 228: obj = 0.7073021, train = 0.7707876, valid = 0.7825434, test = 0.48021019
  Iteration 229: obj = 0.70612645, train = 0.77333426, valid = 0.7816217, test = 0.49313623
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  Iteration 694: obj = 0.64115137, train = 0.6442108, valid = 0.46254033, test = 0.26124322
  Iteration 701: obj = 0.64218485, train = 0.6482081, valid = 0.4624247, test = 0.27939934
  Iteration 744: obj = 0.6366158, train = 0.63790584, valid = 0.45486447, test = 0.25855744
  Iteration 746: obj = 0.6359964, train = 0.6377505, valid = 0.45252192, test = 0.27129388
  Iteration 1000: obj = 0.616598, train = 0.61372054, valid = 0.51261044, test = 0.26105154
(2, 47, 1)
MEM error = 0.450, GRU error = 0.271/0.27129388, better = 1/1
overall MEM error   = 0.450
overall GRU error = 0.271
  Iteration 1: obj = 0.7457699, train = 0.82570755, valid = 0.865975, test = 0.8494255
  Iteration 2: obj = 0.7455602, train = 0.8254904, valid = 0.86518925, test = 0.8485843
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  Iteration 1551: obj = 0.5652723, train = 0.5196636, valid = 0.44503337, test = 0.55110747
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  Iteration 2164: obj = 0.54248625, train = 0.4687278, valid = 0.41636407, test = 0.65058845
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  Iteration 2381: obj = 0.5357523, train = 0.46778077, valid = 0.38716215, test = 0.5781136
  Iteration 2491: obj = 0.53879493, train = 0.46793872, valid = 0.37777418, test = 0.5981941
  Iteration 2826: obj = 0.5337945, train = 0.46488774, valid = 0.37431997, test = 0.5931101
  Iteration 3000: obj = 0.5344465, train = 0.45814604, valid = 0.43322426, test = 0.5940876
  Iteration 3004: obj = 0.53324765, train = 0.46244174, valid = 0.36821434, test = 0.57211375
  Iteration 3356: obj = 0.52455217, train = 0.45086724, valid = 0.35591882, test = 0.60432005
  Iteration 4000: obj = 0.5263501, train = 0.44464922, valid = 0.45035636, test = 0.5629854
  Iteration 4223: obj = 0.5257163, train = 0.44631922, valid = 0.3509469, test = 0.5678129
  Iteration 4722: obj = 0.52036196, train = 0.42822695, valid = 0.34125793, test = 0.5885693
  Iteration 5000: obj = 0.52035993, train = 0.43159536, valid = 0.41146195, test = 0.58329237
  Iteration 5086: obj = 0.52293575, train = 0.43164527, valid = 0.32666793, test = 0.5798185
  Iteration 6000: obj = 0.5151152, train = 0.4315964, valid = 0.46475112, test = 0.6036732
(4, 47, 1)
MEM error = 0.591, GRU error = 0.579/0.5798185, better = 2/2
overall MEM error   = 0.520
overall GRU error = 0.425
  Iteration 1: obj = 0.76135963, train = 0.827226, valid = 0.8041089, test = 0.865975
  Iteration 2: obj = 0.76100767, train = 0.827104, valid = 0.80400336, test = 0.86514664
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  Iteration 2000: obj = 0.55344635, train = 0.5211308, valid = 0.6240138, test = 0.4373874
  Iteration 2004: obj = 0.55618757, train = 0.52129513, valid = 0.57300913, test = 0.43944585
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  Iteration 2592: obj = 0.5352633, train = 0.4633699, valid = 0.42321104, test = 0.4448861
  Iteration 2687: obj = 0.53899974, train = 0.46021232, valid = 0.41233587, test = 0.43058866
  Iteration 2867: obj = 0.5353254, train = 0.45968276, valid = 0.39765626, test = 0.47058755
  Iteration 2874: obj = 0.53086734, train = 0.4456889, valid = 0.39245054, test = 0.43226147
  Iteration 3000: obj = 0.53135633, train = 0.462242, valid = 0.5042521, test = 0.4523207
  Iteration 3174: obj = 0.52884156, train = 0.44780326, valid = 0.3764572, test = 0.47559285
  Iteration 3313: obj = 0.5294859, train = 0.46152514, valid = 0.36744314, test = 0.43340936
  Iteration 3565: obj = 0.5216713, train = 0.4545955, valid = 0.36585075, test = 0.44544148
  Iteration 3643: obj = 0.522401, train = 0.4402448, valid = 0.3304522, test = 0.4264632
  Iteration 4000: obj = 0.51857686, train = 0.44267792, valid = 0.4455225, test = 0.42105162
(6, 47, 1)
MEM error = 0.558, GRU error = 0.426/0.4264632, better = 3/3
overall MEM error   = 0.533
overall GRU error = 0.425
  Iteration 1: obj = 0.7556903, train = 0.8215563, valid = 0.90351367, test = 0.8041089
  Iteration 2: obj = 0.75510466, train = 0.82145274, valid = 0.9034786, test = 0.8040203
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(8, 47, 1)
MEM error = 0.355, GRU error = 0.391/0.39101085, better = 3/4
overall MEM error   = 0.488
overall GRU error = 0.417
  Iteration 1: obj = 0.74552035, train = 0.795793, valid = 0.91504025, test = 0.90351367
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  Iteration 13123: obj = 0.44275934, train = 0.3054027, valid = 0.6661334, test = 0.5448172
  Iteration 13144: obj = 0.44926283, train = 0.3105786, valid = 0.6655661, test = 0.54403925
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  Iteration 13641: obj = 0.4479182, train = 0.3139877, valid = 0.66531354, test = 0.543913
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  Iteration 13677: obj = 0.4424575, train = 0.3116933, valid = 0.66492003, test = 0.5429261
  Iteration 13779: obj = 0.4421441, train = 0.31282735, valid = 0.66488695, test = 0.5437934
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  Iteration 13937: obj = 0.44927135, train = 0.31127834, valid = 0.66414094, test = 0.5415275
  Iteration 13938: obj = 0.44700488, train = 0.3056059, valid = 0.66402674, test = 0.5416007
  Iteration 13939: obj = 0.44686458, train = 0.3136842, valid = 0.6640147, test = 0.54179
  Iteration 14000: obj = 0.444666, train = 0.3156994, valid = 0.66514635, test = 0.54351693
  Iteration 14005: obj = 0.44174704, train = 0.3073878, valid = 0.66393656, test = 0.54238755
  Iteration 14006: obj = 0.4429923, train = 0.31846935, valid = 0.6639056, test = 0.5423137
  Iteration 14007: obj = 0.44907695, train = 0.3251919, valid = 0.66389287, test = 0.5422601
  Iteration 14185: obj = 0.44571865, train = 0.3126402, valid = 0.66388595, test = 0.5426942
  Iteration 14186: obj = 0.44774804, train = 0.32273817, valid = 0.66373205, test = 0.54255444
  Iteration 14189: obj = 0.44799486, train = 0.321692, valid = 0.6636626, test = 0.5424907
  Iteration 15000: obj = 0.44219545, train = 0.3224495, valid = 0.66542757, test = 0.54131985
(10, 47, 1)
MEM error = 0.687, GRU error = 0.542/0.5424907, better = 4/5
overall MEM error   = 0.528
overall GRU error = 0.442
  Iteration 1: obj = 0.7612172, train = 0.83559537, valid = 0.6009703, test = 0.91504025
  Iteration 2: obj = 0.76096773, train = 0.835569, valid = 0.60093546, test = 0.91515815
  Iteration 3: obj = 0.7606503, train = 0.83554226, valid = 0.6009007, test = 0.91507083
  Iteration 4: obj = 0.7602028, train = 0.8355148, valid = 0.60086465, test = 0.9150859
  Iteration 5: obj = 0.7600728, train = 0.8355267, valid = 0.5950177, test = 0.9151182
  Iteration 13: obj = 0.75737333, train = 0.8268746, valid = 0.59442353, test = 0.91527325
  Iteration 14: obj = 0.7572329, train = 0.8261719, valid = 0.5939309, test = 0.91525775
  Iteration 15: obj = 0.7568636, train = 0.8254725, valid = 0.5931188, test = 0.9152484
  Iteration 16: obj = 0.75665385, train = 0.82477665, valid = 0.5923079, test = 0.91523397
  Iteration 17: obj = 0.75638455, train = 0.82424563, valid = 0.59142464, test = 0.9152181
  Iteration 18: obj = 0.7560184, train = 0.8238597, valid = 0.58895004, test = 0.9152008
  Iteration 19: obj = 0.7555592, train = 0.82315385, valid = 0.5884555, test = 0.91518235
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  Iteration 22: obj = 0.75466347, train = 0.8210496, valid = 0.5874788, test = 0.9151194
  Iteration 23: obj = 0.7543338, train = 0.82035244, valid = 0.58668077, test = 0.91509616
  Iteration 24: obj = 0.75419605, train = 0.8192427, valid = 0.5858849, test = 0.9150717
  Iteration 25: obj = 0.75363296, train = 0.8181118, valid = 0.5837805, test = 0.9150462
  Iteration 26: obj = 0.75360495, train = 0.8174478, valid = 0.5836714, test = 0.91501975
  Iteration 27: obj = 0.75317794, train = 0.8147189, valid = 0.5835561, test = 0.914992
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  Iteration 32: obj = 0.7516035, train = 0.81274533, valid = 0.58304465, test = 0.9148669
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  Iteration 34: obj = 0.75106764, train = 0.812914, valid = 0.58287156, test = 0.91482365
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  Iteration 45: obj = 0.74781656, train = 0.80521786, valid = 0.5817371, test = 0.91454756
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  Iteration 47: obj = 0.74745154, train = 0.8052982, valid = 0.5814609, test = 0.9144827
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  Iteration 53: obj = 0.74600905, train = 0.8021308, valid = 0.5804827, test = 0.9142566
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  Iteration 55: obj = 0.74512035, train = 0.80144083, valid = 0.58010805, test = 0.91417086
  Iteration 56: obj = 0.744708, train = 0.79911804, valid = 0.5799121, test = 0.9141263
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  Iteration 58: obj = 0.7441786, train = 0.80293286, valid = 0.5794914, test = 0.9140308
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  Iteration 76: obj = 0.7391092, train = 0.7930418, valid = 0.57733214, test = 0.9129868
  Iteration 79: obj = 0.73859024, train = 0.78874916, valid = 0.57712144, test = 0.9125949
  Iteration 80: obj = 0.73770756, train = 0.7931393, valid = 0.5770471, test = 0.9124618
  Iteration 81: obj = 0.73717296, train = 0.7928976, valid = 0.57696986, test = 0.9123271
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  Iteration 84: obj = 0.73682666, train = 0.79218835, valid = 0.57675165, test = 0.9119297
  Iteration 87: obj = 0.73516566, train = 0.78948194, valid = 0.57656056, test = 0.911546
  Iteration 92: obj = 0.73310757, train = 0.78576404, valid = 0.5763103, test = 0.9110051
  Iteration 185: obj = 0.7056915, train = 0.7582451, valid = 0.5743418, test = 0.90566087
  Iteration 221: obj = 0.6978246, train = 0.7444799, valid = 0.5730829, test = 0.902465
  Iteration 222: obj = 0.70027834, train = 0.7385553, valid = 0.56954336, test = 0.9009835
  Iteration 226: obj = 0.69900537, train = 0.7490494, valid = 0.56753767, test = 0.89970744
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  Iteration 287: obj = 0.68330616, train = 0.7238487, valid = 0.5623101, test = 0.8945365
  Iteration 296: obj = 0.6820768, train = 0.7240559, valid = 0.5581571, test = 0.89499164
  Iteration 302: obj = 0.6826965, train = 0.7241794, valid = 0.5552931, test = 0.89294577
  Iteration 310: obj = 0.6780925, train = 0.71466076, valid = 0.5495274, test = 0.890364
  Iteration 314: obj = 0.678668, train = 0.720242, valid = 0.54337937, test = 0.89330125
  Iteration 413: obj = 0.6594508, train = 0.6792736, valid = 0.54253334, test = 0.8762431
  Iteration 414: obj = 0.6573576, train = 0.67387706, valid = 0.5387734, test = 0.8780597
  Iteration 426: obj = 0.6536008, train = 0.66523325, valid = 0.5346199, test = 0.87762713
  Iteration 432: obj = 0.6521714, train = 0.66695285, valid = 0.5304189, test = 0.87610453
  Iteration 443: obj = 0.6493257, train = 0.6574038, valid = 0.527061, test = 0.87319577
  Iteration 527: obj = 0.63356507, train = 0.6315574, valid = 0.52526486, test = 0.86475384
  Iteration 537: obj = 0.63300246, train = 0.63388616, valid = 0.52389777, test = 0.8660944
  Iteration 550: obj = 0.6308325, train = 0.62017834, valid = 0.51639414, test = 0.86502945
  Iteration 584: obj = 0.62374085, train = 0.6138382, valid = 0.51424205, test = 0.86136174
  Iteration 702: obj = 0.6142831, train = 0.6000231, valid = 0.49647826, test = 0.86127186
  Iteration 1000: obj = 0.6002215, train = 0.59460926, valid = 0.5440203, test = 0.8474967
  Iteration 1034: obj = 0.5982174, train = 0.5889259, valid = 0.49391627, test = 0.8409926
  Iteration 1158: obj = 0.5946128, train = 0.5829422, valid = 0.49328467, test = 0.83705485
  Iteration 1491: obj = 0.5828733, train = 0.5829989, valid = 0.48786512, test = 0.82883465
  Iteration 1593: obj = 0.5841022, train = 0.56842446, valid = 0.48491454, test = 0.8277956
  Iteration 1609: obj = 0.5802195, train = 0.55729693, valid = 0.4774533, test = 0.8277486
  Iteration 1657: obj = 0.5764549, train = 0.552521, valid = 0.47528854, test = 0.8267766
  Iteration 1697: obj = 0.5748698, train = 0.559776, valid = 0.46838865, test = 0.8260821
  Iteration 1743: obj = 0.5732996, train = 0.55899143, valid = 0.46389127, test = 0.82142043
  Iteration 1762: obj = 0.57355666, train = 0.5553924, valid = 0.46137828, test = 0.8228097
  Iteration 1768: obj = 0.5754934, train = 0.5550126, valid = 0.46060523, test = 0.8237772
  Iteration 1772: obj = 0.5705744, train = 0.55453634, valid = 0.46016037, test = 0.81901956
  Iteration 1778: obj = 0.57094336, train = 0.53695345, valid = 0.45938975, test = 0.82142735
  Iteration 1780: obj = 0.56963897, train = 0.5379976, valid = 0.45906246, test = 0.8197423
  Iteration 1785: obj = 0.5696349, train = 0.5483639, valid = 0.4588997, test = 0.8204344
  Iteration 1809: obj = 0.5673234, train = 0.53156424, valid = 0.45705533, test = 0.8226968
  Iteration 1814: obj = 0.5702886, train = 0.53952295, valid = 0.45657194, test = 0.8204005
  Iteration 1815: obj = 0.5706417, train = 0.5383496, valid = 0.45650738, test = 0.81659055
  Iteration 1820: obj = 0.5646716, train = 0.52307665, valid = 0.45610058, test = 0.8155705
  Iteration 1825: obj = 0.57063556, train = 0.54688835, valid = 0.45598483, test = 0.82173586
  Iteration 1827: obj = 0.5691032, train = 0.5360466, valid = 0.45587966, test = 0.82149184
  Iteration 1829: obj = 0.5655912, train = 0.5245497, valid = 0.45581585, test = 0.81629777
  Iteration 1833: obj = 0.56621915, train = 0.5282699, valid = 0.4554863, test = 0.8206351
  Iteration 1835: obj = 0.56461835, train = 0.52335465, valid = 0.45519012, test = 0.81605005
  Iteration 1838: obj = 0.5653687, train = 0.52352214, valid = 0.44315314, test = 0.81545174
  Iteration 1897: obj = 0.5636618, train = 0.51673293, valid = 0.44213608, test = 0.81202686
  Iteration 1916: obj = 0.5594825, train = 0.5118861, valid = 0.42996013, test = 0.81233793
  Iteration 1941: obj = 0.560599, train = 0.51238835, valid = 0.42933023, test = 0.8105606
  Iteration 1951: obj = 0.5618759, train = 0.5102127, valid = 0.42444855, test = 0.80892265
  Iteration 1958: obj = 0.56181246, train = 0.51801974, valid = 0.4165396, test = 0.80972576
  Iteration 1967: obj = 0.55904144, train = 0.5052707, valid = 0.41318762, test = 0.8093022
  Iteration 2000: obj = 0.55515754, train = 0.5001028, valid = 0.43091926, test = 0.80527174
  Iteration 2043: obj = 0.5525627, train = 0.5092163, valid = 0.40656477, test = 0.8040514
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  Iteration 2180: obj = 0.55457944, train = 0.50036037, valid = 0.3797654, test = 0.7989874
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  Iteration 2290: obj = 0.5415693, train = 0.46280053, valid = 0.37191638, test = 0.7953273
  Iteration 2313: obj = 0.5380901, train = 0.48395193, valid = 0.37011945, test = 0.7959143
  Iteration 2337: obj = 0.5456339, train = 0.4865656, valid = 0.3692872, test = 0.7939967
  Iteration 2342: obj = 0.5413485, train = 0.48714495, valid = 0.36892915, test = 0.79370725
  Iteration 2350: obj = 0.5423757, train = 0.48739755, valid = 0.36890087, test = 0.79282695
  Iteration 2351: obj = 0.5409546, train = 0.48803055, valid = 0.36502004, test = 0.7926955
  Iteration 2352: obj = 0.5420525, train = 0.48210225, valid = 0.36446658, test = 0.7925647
  Iteration 2361: obj = 0.5436326, train = 0.47569603, valid = 0.36431283, test = 0.79149485
  Iteration 2366: obj = 0.5392515, train = 0.49738753, valid = 0.36313623, test = 0.7910865
  Iteration 2367: obj = 0.53770095, train = 0.48243082, valid = 0.36278552, test = 0.79098547
  Iteration 2368: obj = 0.54033214, train = 0.48593983, valid = 0.3585574, test = 0.7908845
  Iteration 2379: obj = 0.5340977, train = 0.47343862, valid = 0.35757858, test = 0.7900045
  Iteration 2383: obj = 0.5398564, train = 0.48492706, valid = 0.35333255, test = 0.78966653
  Iteration 2390: obj = 0.5365765, train = 0.46919593, valid = 0.34966373, test = 0.7888144
  Iteration 2400: obj = 0.53602606, train = 0.4705841, valid = 0.3490616, test = 0.78716385
  Iteration 2401: obj = 0.53415865, train = 0.47296667, valid = 0.34452346, test = 0.7870351
  Iteration 2411: obj = 0.53784347, train = 0.46612296, valid = 0.34372804, test = 0.78571963
  Iteration 2413: obj = 0.5345137, train = 0.4720968, valid = 0.3428293, test = 0.7854674
  Iteration 2414: obj = 0.5373584, train = 0.47067547, valid = 0.3424511, test = 0.78535205
  Iteration 2415: obj = 0.5396119, train = 0.48112586, valid = 0.34227782, test = 0.7852719
  Iteration 2424: obj = 0.5382803, train = 0.478346, valid = 0.33093905, test = 0.78453517
  Iteration 2439: obj = 0.53255117, train = 0.46749052, valid = 0.32611212, test = 0.7826284
  Iteration 3000: obj = 0.5124308, train = 0.42033112, valid = 0.39913392, test = 0.69961256
(12, 47, 1)
MEM error = 0.784, GRU error = 0.782/0.7826284, better = 5/6
overall MEM error   = 0.571
overall GRU error = 0.498
  Iteration 1: obj = 0.76928854, train = 0.8547795, valid = 0.80022454, test = 0.6009703
  Iteration 2: obj = 0.7689987, train = 0.8546926, valid = 0.8000764, test = 0.6009486
  Iteration 3: obj = 0.76877046, train = 0.8546146, valid = 0.79994226, test = 0.6009426
  Iteration 4: obj = 0.7684236, train = 0.8545377, valid = 0.7998098, test = 0.6009385
  Iteration 5: obj = 0.7680719, train = 0.85446423, valid = 0.7996825, test = 0.6009402
  Iteration 6: obj = 0.76779526, train = 0.8530183, valid = 0.7995475, test = 0.600582
  Iteration 7: obj = 0.76746076, train = 0.8531987, valid = 0.79940724, test = 0.58502895
  Iteration 8: obj = 0.7669511, train = 0.85293746, valid = 0.79926264, test = 0.59890735
  Iteration 9: obj = 0.766911, train = 0.8528939, valid = 0.7991139, test = 0.5978643
  Iteration 10: obj = 0.7666376, train = 0.8528484, valid = 0.7989613, test = 0.59477615
  Iteration 11: obj = 0.7664067, train = 0.8527087, valid = 0.79880464, test = 0.592481
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  Iteration 2000: obj = 0.56935793, train = 0.5320087, valid = 0.57542086, test = 0.42190224
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  Iteration 2016: obj = 0.5682988, train = 0.53582525, valid = 0.57512474, test = 0.42305613
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  Iteration 2270: obj = 0.5617323, train = 0.514104, valid = 0.5426747, test = 0.3954052
  Iteration 2271: obj = 0.55622345, train = 0.5159451, valid = 0.5404192, test = 0.40159106
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  Iteration 2364: obj = 0.55165434, train = 0.50920486, valid = 0.519122, test = 0.4034847
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  Iteration 2445: obj = 0.54637194, train = 0.49031585, valid = 0.5156536, test = 0.42070773
  Iteration 2450: obj = 0.54788804, train = 0.48891857, valid = 0.50853515, test = 0.41109318
  Iteration 2638: obj = 0.5332252, train = 0.47404793, valid = 0.5078915, test = 0.43837002
  Iteration 2658: obj = 0.5304745, train = 0.46664906, valid = 0.49625522, test = 0.43784106
  Iteration 3000: obj = 0.51688415, train = 0.42621142, valid = 0.57756853, test = 0.506912
(14, 47, 1)
MEM error = 0.327, GRU error = 0.437/0.43784106, better = 5/7
overall MEM error   = 0.536
overall GRU error = 0.490
  Iteration 1: obj = 0.7489583, train = 0.8469149, valid = 0.7782135, test = 0.80022454
  Iteration 2: obj = 0.7486801, train = 0.8464085, valid = 0.77805686, test = 0.79994774
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  Iteration 187: obj = 0.70395494, train = 0.7890855, valid = 0.6305649, test = 0.7186885
  Iteration 200: obj = 0.7012185, train = 0.7883805, valid = 0.62240887, test = 0.7104032
  Iteration 337: obj = 0.6755792, train = 0.7235255, valid = 0.58906955, test = 0.6663451
  Iteration 515: obj = 0.6408074, train = 0.64389616, valid = 0.5807226, test = 0.6349144
  Iteration 518: obj = 0.6425271, train = 0.6391045, valid = 0.5773028, test = 0.62077093
  Iteration 523: obj = 0.6382367, train = 0.63380444, valid = 0.5750809, test = 0.6234579
  Iteration 528: obj = 0.6386762, train = 0.63710797, valid = 0.57263017, test = 0.6137399
  Iteration 535: obj = 0.63596994, train = 0.62567836, valid = 0.56622076, test = 0.62554336
  Iteration 540: obj = 0.6366192, train = 0.6357979, valid = 0.5629412, test = 0.62009245
  Iteration 564: obj = 0.6326442, train = 0.62122405, valid = 0.5603895, test = 0.61876035
  Iteration 570: obj = 0.63256896, train = 0.62705976, valid = 0.5553465, test = 0.626809
  Iteration 571: obj = 0.6317076, train = 0.6222439, valid = 0.55316055, test = 0.6257838
  Iteration 572: obj = 0.6308364, train = 0.6233356, valid = 0.55212426, test = 0.60546005
  Iteration 575: obj = 0.6299642, train = 0.6188485, valid = 0.54902685, test = 0.6132678
  Iteration 576: obj = 0.62941, train = 0.6173936, valid = 0.54800475, test = 0.61276686
  Iteration 577: obj = 0.6318602, train = 0.62247884, valid = 0.5473002, test = 0.6123533
  Iteration 584: obj = 0.6288328, train = 0.61915755, valid = 0.5405518, test = 0.6111031
  Iteration 588: obj = 0.6268779, train = 0.61901575, valid = 0.5342442, test = 0.611902
  Iteration 601: obj = 0.6263585, train = 0.613465, valid = 0.5339279, test = 0.6060707
  Iteration 603: obj = 0.62386066, train = 0.6103431, valid = 0.5269685, test = 0.61667496
  Iteration 606: obj = 0.625141, train = 0.6144055, valid = 0.5230619, test = 0.624854
  Iteration 622: obj = 0.6251645, train = 0.6070151, valid = 0.52051616, test = 0.6101869
  Iteration 624: obj = 0.6229887, train = 0.60929763, valid = 0.5174978, test = 0.62084925
  Iteration 625: obj = 0.62210184, train = 0.61161137, valid = 0.5085765, test = 0.6197444
  Iteration 650: obj = 0.61971176, train = 0.600548, valid = 0.50615454, test = 0.6203524
  Iteration 655: obj = 0.6174155, train = 0.6041542, valid = 0.50447047, test = 0.61671585
  Iteration 659: obj = 0.617966, train = 0.5990886, valid = 0.4949355, test = 0.6213803
  Iteration 664: obj = 0.6198651, train = 0.6100286, valid = 0.49026537, test = 0.61842114
  Iteration 701: obj = 0.6159503, train = 0.6076975, valid = 0.46593368, test = 0.6192787
  Iteration 875: obj = 0.6033338, train = 0.5885618, valid = 0.44349897, test = 0.61994046
  Iteration 1000: obj = 0.59260297, train = 0.57110155, valid = 0.5248006, test = 0.5988139
  Iteration 1398: obj = 0.5652138, train = 0.5323006, valid = 0.43986174, test = 0.5519473
  Iteration 1507: obj = 0.56321293, train = 0.5357082, valid = 0.43405414, test = 0.5437528
  Iteration 1509: obj = 0.5653204, train = 0.5345508, valid = 0.42956468, test = 0.54736024
  Iteration 1532: obj = 0.56169444, train = 0.52997744, valid = 0.42335007, test = 0.54573655
  Iteration 1645: obj = 0.5600225, train = 0.51829445, valid = 0.40471056, test = 0.5270197
  Iteration 1688: obj = 0.5550288, train = 0.5129751, valid = 0.38479316, test = 0.5145643
  Iteration 2000: obj = 0.5352575, train = 0.45827362, valid = 0.54880154, test = 0.476462
  Iteration 2214: obj = 0.5341299, train = 0.45705688, valid = 0.38069597, test = 0.46560496
  Iteration 3000: obj = 0.5129333, train = 0.40838814, valid = 0.5110259, test = 0.44857085
(16, 47, 1)
MEM error = 0.501, GRU error = 0.465/0.46560496, better = 6/8
overall MEM error   = 0.532
overall GRU error = 0.487
  Iteration 1: obj = 0.7466189, train = 0.84385073, valid = 0.8320466, test = 0.7782135
  Iteration 2: obj = 0.7464266, train = 0.8432344, valid = 0.83187443, test = 0.77810407
  Iteration 3: obj = 0.7462335, train = 0.8426176, valid = 0.8317034, test = 0.77798855
  Iteration 4: obj = 0.745944, train = 0.8425237, valid = 0.8315326, test = 0.7778712
  Iteration 5: obj = 0.7457051, train = 0.84243464, valid = 0.8313614, test = 0.7777538
  Iteration 6: obj = 0.7455051, train = 0.84101653, valid = 0.830422, test = 0.77763665
  Iteration 7: obj = 0.7453696, train = 0.8422551, valid = 0.8293428, test = 0.77752036
  Iteration 8: obj = 0.7449387, train = 0.8399153, valid = 0.82791007, test = 0.7774054
  Iteration 9: obj = 0.744811, train = 0.842034, valid = 0.8271977, test = 0.77668947
  Iteration 10: obj = 0.7446741, train = 0.84150904, valid = 0.8244899, test = 0.77589625
  Iteration 12: obj = 0.7442847, train = 0.8403435, valid = 0.8240141, test = 0.7743127
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  Iteration 14: obj = 0.7438367, train = 0.8393575, valid = 0.8219147, test = 0.7727325
  Iteration 15: obj = 0.74350363, train = 0.83949566, valid = 0.8208716, test = 0.7719443
  Iteration 16: obj = 0.74326706, train = 0.8397198, valid = 0.8198329, test = 0.771157
  Iteration 17: obj = 0.7429772, train = 0.83679116, valid = 0.81879884, test = 0.77037126
  Iteration 18: obj = 0.742818, train = 0.83846647, valid = 0.8179968, test = 0.7695865
  Iteration 19: obj = 0.7426429, train = 0.83865565, valid = 0.817852, test = 0.76880294
  Iteration 20: obj = 0.74253875, train = 0.83722794, valid = 0.81770706, test = 0.7680207
  Iteration 21: obj = 0.7422393, train = 0.8366135, valid = 0.81756127, test = 0.7672396
  Iteration 22: obj = 0.7419666, train = 0.8365105, valid = 0.815539, test = 0.76645994
  Iteration 27: obj = 0.7409404, train = 0.83690464, valid = 0.80357265, test = 0.762581
  Iteration 30: obj = 0.74011344, train = 0.8366026, valid = 0.8017523, test = 0.7602668
  Iteration 48: obj = 0.7365941, train = 0.831648, valid = 0.8009104, test = 0.746596
  Iteration 53: obj = 0.73557043, train = 0.8287945, valid = 0.80055106, test = 0.7428761
  Iteration 54: obj = 0.73556143, train = 0.82788557, valid = 0.79850924, test = 0.74213684
  Iteration 56: obj = 0.73520374, train = 0.8262826, valid = 0.77985436, test = 0.7406651
  Iteration 71: obj = 0.7322136, train = 0.8219602, valid = 0.77812195, test = 0.72345084
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  Iteration 73: obj = 0.731708, train = 0.82351565, valid = 0.77731884, test = 0.71846926
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  Iteration 76: obj = 0.73124343, train = 0.8235336, valid = 0.77470934, test = 0.7231777
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  Iteration 78: obj = 0.73047626, train = 0.82167256, valid = 0.77232563, test = 0.71864563
  Iteration 80: obj = 0.7301659, train = 0.8232093, valid = 0.76958865, test = 0.71772486
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  Iteration 84: obj = 0.7291181, train = 0.8203899, valid = 0.7683226, test = 0.7157054
  Iteration 85: obj = 0.729461, train = 0.82001185, valid = 0.767542, test = 0.71357805
  Iteration 86: obj = 0.7289854, train = 0.8197247, valid = 0.76712257, test = 0.7094433
  Iteration 89: obj = 0.7288428, train = 0.8159479, valid = 0.76584643, test = 0.7119715
  Iteration 90: obj = 0.72822076, train = 0.81600857, valid = 0.7654195, test = 0.7103127
  Iteration 91: obj = 0.72726977, train = 0.81769514, valid = 0.764992, test = 0.7111675
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  Iteration 97: obj = 0.72626585, train = 0.81532025, valid = 0.7623836, test = 0.70540273
  Iteration 99: obj = 0.7254971, train = 0.81279874, valid = 0.7614995, test = 0.70797163
  Iteration 100: obj = 0.72560304, train = 0.81365347, valid = 0.76105666, test = 0.7060248
  Iteration 101: obj = 0.7258607, train = 0.81372666, valid = 0.76061225, test = 0.7057824
  Iteration 102: obj = 0.7253648, train = 0.81332266, valid = 0.76016617, test = 0.70158464
  Iteration 103: obj = 0.7246934, train = 0.8126302, valid = 0.7597187, test = 0.70191634
  Iteration 104: obj = 0.7248494, train = 0.81313705, valid = 0.7592675, test = 0.705996
  Iteration 105: obj = 0.72517973, train = 0.81290674, valid = 0.7588144, test = 0.70359766
  Iteration 106: obj = 0.7244235, train = 0.81270623, valid = 0.7583591, test = 0.70427334
  Iteration 107: obj = 0.7248878, train = 0.8132223, valid = 0.75790054, test = 0.7049535
  Iteration 108: obj = 0.7241238, train = 0.8128862, valid = 0.75743943, test = 0.70563614
  Iteration 109: obj = 0.72415346, train = 0.81043756, valid = 0.7569757, test = 0.7063217
  Iteration 110: obj = 0.72310823, train = 0.80897665, valid = 0.7565086, test = 0.69719493
  Iteration 111: obj = 0.7238283, train = 0.81035876, valid = 0.7560383, test = 0.69539
  Iteration 112: obj = 0.7234525, train = 0.81044966, valid = 0.75556517, test = 0.7045677
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  Iteration 116: obj = 0.72190726, train = 0.80743134, valid = 0.7536403, test = 0.702654
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  Iteration 118: obj = 0.72231925, train = 0.80821073, valid = 0.7526684, test = 0.7126477
  Iteration 119: obj = 0.72038275, train = 0.8107911, valid = 0.752181, test = 0.69602776
  Iteration 120: obj = 0.72228724, train = 0.80998486, valid = 0.7516916, test = 0.7161196
  Iteration 121: obj = 0.7214178, train = 0.80752474, valid = 0.7512013, test = 0.7025279
  Iteration 122: obj = 0.7213287, train = 0.8073199, valid = 0.7507098, test = 0.69521904
  Iteration 123: obj = 0.7216769, train = 0.8086347, valid = 0.7502142, test = 0.7012541
  Iteration 124: obj = 0.7205803, train = 0.80389833, valid = 0.74970984, test = 0.6977176
  Iteration 125: obj = 0.72065157, train = 0.80696005, valid = 0.7492043, test = 0.700135
  Iteration 126: obj = 0.7200929, train = 0.8117552, valid = 0.7486968, test = 0.69266367
  Iteration 127: obj = 0.7195944, train = 0.80273044, valid = 0.74818766, test = 0.6905447
  Iteration 129: obj = 0.71965826, train = 0.8033097, valid = 0.74716455, test = 0.6903677
  Iteration 130: obj = 0.7202719, train = 0.80115294, valid = 0.7466476, test = 0.6909677
  Iteration 131: obj = 0.71899486, train = 0.80119246, valid = 0.7461293, test = 0.69120765
  Iteration 132: obj = 0.7181991, train = 0.80495346, valid = 0.74560773, test = 0.6934724
  Iteration 133: obj = 0.71920854, train = 0.7981055, valid = 0.7450815, test = 0.6881707
  Iteration 134: obj = 0.7183105, train = 0.7992772, valid = 0.74455225, test = 0.68154824
  Iteration 136: obj = 0.7175754, train = 0.80037713, valid = 0.7434857, test = 0.6865505
  Iteration 137: obj = 0.7185076, train = 0.7978356, valid = 0.7431685, test = 0.70237327
  Iteration 139: obj = 0.7168734, train = 0.79437107, valid = 0.7425269, test = 0.6867367
  Iteration 140: obj = 0.7176347, train = 0.8029423, valid = 0.7421998, test = 0.6863665
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  Iteration 143: obj = 0.7180394, train = 0.80209774, valid = 0.7412084, test = 0.68446803
  Iteration 144: obj = 0.71592516, train = 0.79718876, valid = 0.74087226, test = 0.70133394
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  Iteration 150: obj = 0.7151798, train = 0.7967446, valid = 0.73946154, test = 0.67338157
  Iteration 153: obj = 0.7150657, train = 0.8010192, valid = 0.737717, test = 0.68034434
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  Iteration 274: obj = 0.6935851, train = 0.74487793, valid = 0.67094827, test = 0.73393214
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  Iteration 342: obj = 0.6801711, train = 0.7162525, valid = 0.66065073, test = 0.70047176
  Iteration 389: obj = 0.67159045, train = 0.68835425, valid = 0.65483737, test = 0.668036
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  Iteration 401: obj = 0.6700979, train = 0.6854284, valid = 0.6361498, test = 0.6871717
  Iteration 415: obj = 0.6670697, train = 0.6886015, valid = 0.63546425, test = 0.68514705
  Iteration 416: obj = 0.66832227, train = 0.6860625, valid = 0.63395995, test = 0.64835477
  Iteration 417: obj = 0.6642061, train = 0.68588454, valid = 0.63386595, test = 0.6608224
  Iteration 418: obj = 0.6687467, train = 0.6751734, valid = 0.630936, test = 0.6468809
  Iteration 419: obj = 0.66681975, train = 0.6835317, valid = 0.62941754, test = 0.6925401
  Iteration 422: obj = 0.6653071, train = 0.6777752, valid = 0.62487143, test = 0.6723163
  Iteration 424: obj = 0.6641732, train = 0.6841214, valid = 0.6226987, test = 0.6831728
  Iteration 425: obj = 0.6647674, train = 0.6767392, valid = 0.6203043, test = 0.67659026
  Iteration 426: obj = 0.6661034, train = 0.68506265, valid = 0.6187655, test = 0.6696293
  Iteration 427: obj = 0.66380364, train = 0.6813351, valid = 0.61722636, test = 0.6589116
  Iteration 431: obj = 0.6645788, train = 0.6807443, valid = 0.61288476, test = 0.6682384
  Iteration 433: obj = 0.6644433, train = 0.6770966, valid = 0.6079811, test = 0.6476333
  Iteration 443: obj = 0.66121364, train = 0.6695764, valid = 0.6016408, test = 0.6662399
  Iteration 451: obj = 0.658831, train = 0.6742822, valid = 0.59612817, test = 0.6543137
  Iteration 463: obj = 0.65829784, train = 0.66227996, valid = 0.59533703, test = 0.65187085
  Iteration 464: obj = 0.6574985, train = 0.6652555, valid = 0.5943812, test = 0.615021
  Iteration 467: obj = 0.65841436, train = 0.66497326, valid = 0.5914936, test = 0.6882359
  Iteration 469: obj = 0.6592346, train = 0.6684442, valid = 0.5900034, test = 0.68019843
  Iteration 470: obj = 0.6565095, train = 0.6605629, valid = 0.58862066, test = 0.68303764
  Iteration 477: obj = 0.65691155, train = 0.65686315, valid = 0.5840972, test = 0.6574708
  Iteration 478: obj = 0.6543635, train = 0.6548871, valid = 0.583873, test = 0.6483796
  Iteration 480: obj = 0.65454316, train = 0.6651832, valid = 0.58071494, test = 0.65428674
  Iteration 484: obj = 0.65733933, train = 0.6620068, valid = 0.5752344, test = 0.68308926
  Iteration 492: obj = 0.65459, train = 0.6541269, valid = 0.5716698, test = 0.66806483
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  Iteration 501: obj = 0.6515024, train = 0.6513492, valid = 0.5645319, test = 0.63681805
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  Iteration 529: obj = 0.6495076, train = 0.6347426, valid = 0.5448371, test = 0.6094725
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  Iteration 556: obj = 0.6423384, train = 0.62395275, valid = 0.53929913, test = 0.61521995
  Iteration 562: obj = 0.6402931, train = 0.62660944, valid = 0.53375113, test = 0.6196618
  Iteration 604: obj = 0.63506633, train = 0.61647385, valid = 0.53301954, test = 0.5775446
  Iteration 608: obj = 0.63302815, train = 0.6122637, valid = 0.5299324, test = 0.59651816
  Iteration 611: obj = 0.6298721, train = 0.61070013, valid = 0.52516043, test = 0.5315808
  Iteration 618: obj = 0.63071865, train = 0.6023008, valid = 0.5142046, test = 0.6098309
  Iteration 619: obj = 0.63022333, train = 0.60529816, valid = 0.51151675, test = 0.5926075
  Iteration 687: obj = 0.6213457, train = 0.5921861, valid = 0.51096594, test = 0.55403113
  Iteration 717: obj = 0.6170332, train = 0.5902755, valid = 0.51026386, test = 0.6007438
  Iteration 762: obj = 0.6119849, train = 0.5826405, valid = 0.5098101, test = 0.6202123
  Iteration 820: obj = 0.603482, train = 0.5682348, valid = 0.50441056, test = 0.56463325
  Iteration 1000: obj = 0.5833907, train = 0.5453564, valid = 0.58348143, test = 0.71583676
  Iteration 1072: obj = 0.57383126, train = 0.5333699, valid = 0.50410616, test = 0.5633932
  Iteration 1184: obj = 0.56772035, train = 0.5216068, valid = 0.48439878, test = 0.8322572
  Iteration 1451: obj = 0.5487989, train = 0.48100168, valid = 0.48052037, test = 0.5935354
  Iteration 1558: obj = 0.54776883, train = 0.47727138, valid = 0.47674447, test = 0.6874894
  Iteration 1610: obj = 0.5409852, train = 0.46935862, valid = 0.4741693, test = 0.5803193
  Iteration 1621: obj = 0.5455204, train = 0.472663, valid = 0.4665022, test = 0.6899644
  Iteration 1704: obj = 0.54354006, train = 0.47496682, valid = 0.45899802, test = 0.614963
  Iteration 1716: obj = 0.5418623, train = 0.47759035, valid = 0.4572611, test = 0.6028985
  Iteration 1721: obj = 0.54063696, train = 0.48337504, valid = 0.44058397, test = 0.914616
  Iteration 1745: obj = 0.54008454, train = 0.46969402, valid = 0.4341943, test = 0.64453954
  Iteration 1883: obj = 0.5418379, train = 0.4798013, valid = 0.423722, test = 0.56096935
  Iteration 1923: obj = 0.5400338, train = 0.47709966, valid = 0.4236465, test = 0.7138715
  Iteration 1964: obj = 0.535229, train = 0.4626618, valid = 0.4181872, test = 0.71653855
  Iteration 2000: obj = 0.53459173, train = 0.46645784, valid = 0.58698833, test = 0.5985869
  Iteration 2037: obj = 0.54241854, train = 0.47317058, valid = 0.41246524, test = 0.61844313
  Iteration 2091: obj = 0.53695023, train = 0.46030384, valid = 0.4100262, test = 0.7044689
  Iteration 2134: obj = 0.53379244, train = 0.4659397, valid = 0.40872943, test = 0.63160706
  Iteration 2358: obj = 0.5365471, train = 0.45274872, valid = 0.40852067, test = 0.5804519
  Iteration 2359: obj = 0.5314954, train = 0.45261478, valid = 0.39177364, test = 0.532006
  Iteration 2399: obj = 0.53340536, train = 0.4569632, valid = 0.38993984, test = 0.58762825
  Iteration 2491: obj = 0.53289026, train = 0.45344174, valid = 0.3890831, test = 0.99103034
  Iteration 2681: obj = 0.5249156, train = 0.44426662, valid = 0.3832035, test = 0.5531412
  Iteration 3000: obj = 0.527603, train = 0.4601285, valid = 0.43689668, test = 0.5878193
(18, 47, 1)
MEM error = 0.918, GRU error = 0.553/0.5531412, better = 7/9
overall MEM error   = 0.575
overall GRU error = 0.494
  Iteration 1: obj = 0.74378574, train = 0.83458203, valid = 0.8953195, test = 0.82971126
  Iteration 2: obj = 0.74348205, train = 0.83444834, valid = 0.8945256, test = 0.8242613
  Iteration 3: obj = 0.7434259, train = 0.83431447, valid = 0.89354753, test = 0.8317158
  Iteration 4: obj = 0.74333173, train = 0.8341803, valid = 0.8925727, test = 0.83155036
  Iteration 5: obj = 0.7429611, train = 0.83404565, valid = 0.8916011, test = 0.8332039
  Iteration 6: obj = 0.74274194, train = 0.83391035, valid = 0.8906335, test = 0.8285539
  Iteration 7: obj = 0.74263674, train = 0.8337743, valid = 0.8896698, test = 0.82933724
  Iteration 8: obj = 0.7422579, train = 0.8336377, valid = 0.8887104, test = 0.8282615
  Iteration 9: obj = 0.7421327, train = 0.8335004, valid = 0.88775575, test = 0.8271903
  Iteration 10: obj = 0.742059, train = 0.8333622, valid = 0.88680553, test = 0.8261235
  Iteration 11: obj = 0.7417001, train = 0.8327537, valid = 0.88586015, test = 0.8250615
  Iteration 12: obj = 0.7415159, train = 0.8320648, valid = 0.8849197, test = 0.8240038
  Iteration 13: obj = 0.7413114, train = 0.83137786, valid = 0.88398445, test = 0.82295096
  Iteration 14: obj = 0.7412258, train = 0.8306924, valid = 0.8830544, test = 0.82190245
  Iteration 15: obj = 0.7409754, train = 0.8300088, valid = 0.8821297, test = 0.8208587
  Iteration 16: obj = 0.7405875, train = 0.8296829, valid = 0.8811003, test = 0.8198196
  Iteration 17: obj = 0.74044186, train = 0.8294985, valid = 0.8796975, test = 0.8187853
  Iteration 18: obj = 0.7403768, train = 0.8293129, valid = 0.8783029, test = 0.8180853
  Iteration 19: obj = 0.7402642, train = 0.8291261, valid = 0.87691665, test = 0.81793964
  Iteration 20: obj = 0.7398914, train = 0.82893825, valid = 0.8755392, test = 0.817793
  Iteration 21: obj = 0.739825, train = 0.82874835, valid = 0.87417305, test = 0.8176445
  Iteration 22: obj = 0.73955524, train = 0.8284284, valid = 0.872818, test = 0.8174943
  Iteration 23: obj = 0.7393409, train = 0.82779974, valid = 0.8714744, test = 0.8173424
  Iteration 24: obj = 0.73908645, train = 0.8271283, valid = 0.8701435, test = 0.8063887
  Iteration 25: obj = 0.7390295, train = 0.8234503, valid = 0.8688229, test = 0.81682837
  Iteration 26: obj = 0.73875344, train = 0.8257896, valid = 0.8675122, test = 0.8162708
  Iteration 27: obj = 0.7385102, train = 0.82218933, valid = 0.85342586, test = 0.80889183
  Iteration 36: obj = 0.7368657, train = 0.8176035, valid = 0.85081965, test = 0.8014886
  Iteration 41: obj = 0.7359275, train = 0.8161329, valid = 0.8503388, test = 0.79841626
  Iteration 42: obj = 0.7353835, train = 0.81584555, valid = 0.84924096, test = 0.79809344
  Iteration 43: obj = 0.73535675, train = 0.81555605, valid = 0.8486725, test = 0.8072076
  Iteration 44: obj = 0.7352363, train = 0.8152646, valid = 0.84854555, test = 0.8067006
  Iteration 46: obj = 0.73481447, train = 0.8146754, valid = 0.84790075, test = 0.805696
  Iteration 47: obj = 0.7344618, train = 0.81437737, valid = 0.84765327, test = 0.8051987
  Iteration 49: obj = 0.73417926, train = 0.8137742, valid = 0.84717387, test = 0.804215
  Iteration 51: obj = 0.73374826, train = 0.8129895, valid = 0.8470938, test = 0.8032421
  Iteration 52: obj = 0.73381275, train = 0.81230736, valid = 0.84685373, test = 0.80059946
  Iteration 54: obj = 0.73319453, train = 0.81190103, valid = 0.8465171, test = 0.7977123
  Iteration 55: obj = 0.7327618, train = 0.81172895, valid = 0.84581196, test = 0.7989516
  Iteration 58: obj = 0.73234814, train = 0.8112123, valid = 0.8456842, test = 0.79383826
  Iteration 59: obj = 0.73211944, train = 0.81123656, valid = 0.8454077, test = 0.79264534
  Iteration 60: obj = 0.73179823, train = 0.81040823, valid = 0.8452575, test = 0.7835014
  Iteration 62: obj = 0.7315086, train = 0.81170994, valid = 0.84492517, test = 0.7877283
  Iteration 63: obj = 0.7311166, train = 0.81075907, valid = 0.84491897, test = 0.7848287
  Iteration 64: obj = 0.7313047, train = 0.810572, valid = 0.8446883, test = 0.7878765
  Iteration 65: obj = 0.7306676, train = 0.8091004, valid = 0.84466785, test = 0.7889426
  Iteration 66: obj = 0.7307485, train = 0.8107782, valid = 0.84337234, test = 0.78326976
  Iteration 70: obj = 0.7297219, train = 0.80980444, valid = 0.842921, test = 0.7771869
  Iteration 71: obj = 0.72967803, train = 0.80957747, valid = 0.8423711, test = 0.7745819
  Iteration 72: obj = 0.7287952, train = 0.80960566, valid = 0.8398614, test = 0.7755803
  Iteration 76: obj = 0.72866285, train = 0.8115586, valid = 0.83973706, test = 0.7772119
  Iteration 77: obj = 0.728504, train = 0.81024045, valid = 0.83923984, test = 0.77584094
  Iteration 78: obj = 0.7284177, train = 0.81048036, valid = 0.8387497, test = 0.7706435
  Iteration 79: obj = 0.7277643, train = 0.8101435, valid = 0.8382666, test = 0.7745624
  Iteration 80: obj = 0.7279136, train = 0.8095604, valid = 0.8377905, test = 0.7698953
  Iteration 81: obj = 0.72794265, train = 0.8094644, valid = 0.8373208, test = 0.7745727
  Iteration 82: obj = 0.7274301, train = 0.81097263, valid = 0.83685756, test = 0.7800713
  Iteration 83: obj = 0.7269198, train = 0.8096853, valid = 0.836403, test = 0.7675781
  Iteration 84: obj = 0.7266243, train = 0.8056145, valid = 0.8359542, test = 0.77062607
  Iteration 85: obj = 0.7268062, train = 0.809083, valid = 0.8355139, test = 0.7666523
  Iteration 86: obj = 0.7264438, train = 0.8096404, valid = 0.83508253, test = 0.770236
  Iteration 87: obj = 0.7261133, train = 0.80947113, valid = 0.83466005, test = 0.76577854
  Iteration 88: obj = 0.72589326, train = 0.80929184, valid = 0.8342486, test = 0.7653364
  Iteration 89: obj = 0.7259414, train = 0.8011801, valid = 0.8338411, test = 0.77010506
  Iteration 90: obj = 0.72600734, train = 0.8107017, valid = 0.83344114, test = 0.7754775
  Iteration 91: obj = 0.7251031, train = 0.80700624, valid = 0.83228254, test = 0.7639931
  Iteration 94: obj = 0.72528285, train = 0.80778444, valid = 0.83192194, test = 0.76262015
  Iteration 95: obj = 0.7246957, train = 0.80426335, valid = 0.8315588, test = 0.7641667
  Iteration 96: obj = 0.7239962, train = 0.8068792, valid = 0.8311955, test = 0.7667628
  Iteration 97: obj = 0.7234232, train = 0.7983562, valid = 0.830843, test = 0.76122755
  Iteration 98: obj = 0.72411656, train = 0.80456287, valid = 0.83049345, test = 0.7607613
  Iteration 99: obj = 0.72434956, train = 0.80705357, valid = 0.83015144, test = 0.76029325
  Iteration 100: obj = 0.72316706, train = 0.8034383, valid = 0.8298125, test = 0.7598241
  Iteration 101: obj = 0.7228177, train = 0.80918324, valid = 0.8294867, test = 0.7593502
  Iteration 102: obj = 0.7235688, train = 0.8035538, valid = 0.8001573, test = 0.7588737
  Iteration 108: obj = 0.72205573, train = 0.80174696, valid = 0.78610015, test = 0.7559793
  Iteration 113: obj = 0.7204501, train = 0.7997879, valid = 0.78064185, test = 0.75354373
  Iteration 116: obj = 0.72080356, train = 0.79843247, valid = 0.77073073, test = 0.75206053
  Iteration 119: obj = 0.71914846, train = 0.79025745, valid = 0.768223, test = 0.7505545
  Iteration 125: obj = 0.7182966, train = 0.79925096, valid = 0.7522909, test = 0.7474589
  Iteration 136: obj = 0.7162965, train = 0.8011378, valid = 0.742918, test = 0.74318516
  Iteration 147: obj = 0.71291596, train = 0.7899885, valid = 0.7304426, test = 0.7384052
  Iteration 150: obj = 0.71274203, train = 0.7888752, valid = 0.71890897, test = 0.74940866
  Iteration 155: obj = 0.7121725, train = 0.7839873, valid = 0.715749, test = 0.7327718
  Iteration 158: obj = 0.7122447, train = 0.78924656, valid = 0.70785654, test = 0.7553425
  Iteration 163: obj = 0.71088606, train = 0.792818, valid = 0.70570993, test = 0.7403691
  Iteration 176: obj = 0.70985025, train = 0.78535354, valid = 0.70415914, test = 0.7326063
  Iteration 178: obj = 0.7091744, train = 0.7839268, valid = 0.7029934, test = 0.7261886
  Iteration 180: obj = 0.7086344, train = 0.7861066, valid = 0.70172954, test = 0.73397934
  Iteration 187: obj = 0.7067344, train = 0.7784228, valid = 0.69796705, test = 0.7427258
  Iteration 192: obj = 0.7053213, train = 0.78400326, valid = 0.69205743, test = 0.72007763
  Iteration 204: obj = 0.7030377, train = 0.7743553, valid = 0.6902282, test = 0.71479094
  Iteration 212: obj = 0.70299256, train = 0.77230406, valid = 0.68795556, test = 0.6830535
  Iteration 409: obj = 0.6694066, train = 0.6719081, valid = 0.68187976, test = 0.63694006
  Iteration 459: obj = 0.65964615, train = 0.66082835, valid = 0.6804569, test = 0.58861727
  Iteration 464: obj = 0.6602254, train = 0.65756583, valid = 0.677894, test = 0.598173
  Iteration 477: obj = 0.6539515, train = 0.645233, valid = 0.66714764, test = 0.58410966
  Iteration 584: obj = 0.6340091, train = 0.6015303, valid = 0.66360927, test = 0.5438118
  Iteration 617: obj = 0.6293653, train = 0.601019, valid = 0.6586875, test = 0.54904485
  Iteration 625: obj = 0.6275706, train = 0.5967413, valid = 0.648286, test = 0.55403864
  Iteration 716: obj = 0.61486363, train = 0.5803141, valid = 0.6384239, test = 0.54057384
  Iteration 770: obj = 0.6125043, train = 0.5744486, valid = 0.62772655, test = 0.5330819
  Iteration 837: obj = 0.6078077, train = 0.573611, valid = 0.6274308, test = 0.55538154
  Iteration 972: obj = 0.5880656, train = 0.5541929, valid = 0.60558456, test = 0.53723824
  Iteration 991: obj = 0.5859265, train = 0.55073535, valid = 0.57708126, test = 0.5734816
  Iteration 1000: obj = 0.588121, train = 0.54749376, valid = 0.6475129, test = 0.53257656
  Iteration 1037: obj = 0.5845596, train = 0.54997647, valid = 0.55525976, test = 0.5469744
  Iteration 1051: obj = 0.580978, train = 0.54119015, valid = 0.5404058, test = 0.55817807
  Iteration 1059: obj = 0.5813183, train = 0.5468202, valid = 0.5343177, test = 0.5432334
  Iteration 1080: obj = 0.5798216, train = 0.5328851, valid = 0.5303946, test = 0.5637325
  Iteration 1087: obj = 0.57990134, train = 0.53817326, valid = 0.5011521, test = 0.5371912
  Iteration 1124: obj = 0.57561255, train = 0.5274499, valid = 0.49288416, test = 0.5279564
  Iteration 1137: obj = 0.57487535, train = 0.52980494, valid = 0.4662131, test = 0.53209186
  Iteration 1187: obj = 0.5730593, train = 0.51575416, valid = 0.4655007, test = 0.54858094
  Iteration 1365: obj = 0.55137736, train = 0.4869542, valid = 0.46450442, test = 0.5074347
  Iteration 1383: obj = 0.55363816, train = 0.49071985, valid = 0.43780932, test = 0.5060226
  Iteration 1692: obj = 0.5499987, train = 0.48558939, valid = 0.43334827, test = 0.45055667
  Iteration 1695: obj = 0.5473216, train = 0.50281477, valid = 0.40953165, test = 0.45191723
  Iteration 1887: obj = 0.5393714, train = 0.4815399, valid = 0.40693992, test = 0.4279847
  Iteration 2000: obj = 0.5290043, train = 0.44579747, valid = 0.48481506, test = 0.49764502
  Iteration 2072: obj = 0.5351991, train = 0.4762078, valid = 0.390962, test = 0.41597727
  Iteration 3000: obj = 0.5146779, train = 0.42540544, valid = 0.4660545, test = 0.5283224
(20, 47, 1)
MEM error = 0.604, GRU error = 0.415/0.41597727, better = 8/10
overall MEM error   = 0.578
overall GRU error = 0.486
  Iteration 1: obj = 0.7613247, train = 0.8461877, valid = 0.68609875, test = 0.8953195
  Iteration 2: obj = 0.7609733, train = 0.8460473, valid = 0.684436, test = 0.8945256
  Iteration 3: obj = 0.7608678, train = 0.8455947, valid = 0.6823816, test = 0.8935496
  Iteration 5: obj = 0.7603573, train = 0.8443727, valid = 0.681404, test = 0.89160967
  Iteration 6: obj = 0.7600998, train = 0.8424399, valid = 0.6804259, test = 0.8906456
  Iteration 7: obj = 0.7599519, train = 0.8423009, valid = 0.6794498, test = 0.88968617
  Iteration 8: obj = 0.75971043, train = 0.8427144, valid = 0.6784757, test = 0.888731
  Iteration 9: obj = 0.7594235, train = 0.8421346, valid = 0.6775037, test = 0.88778055
  Iteration 10: obj = 0.759308, train = 0.8420508, valid = 0.6765338, test = 0.88683456
  Iteration 11: obj = 0.758821, train = 0.8419666, valid = 0.67556584, test = 0.88589394
  Iteration 12: obj = 0.7588617, train = 0.841882, valid = 0.6746001, test = 0.88360673
  Iteration 13: obj = 0.75848925, train = 0.8417969, valid = 0.6736363, test = 0.8840283
  Iteration 14: obj = 0.75837004, train = 0.8417113, valid = 0.6726748, test = 0.8819907
  Iteration 15: obj = 0.7583602, train = 0.8416251, valid = 0.67171514, test = 0.8821837
  Iteration 16: obj = 0.7579071, train = 0.8413267, valid = 0.6707574, test = 0.87606764
  Iteration 17: obj = 0.7576619, train = 0.84055674, valid = 0.66980183, test = 0.8797432
  Iteration 18: obj = 0.7576391, train = 0.8397896, valid = 0.6688482, test = 0.87835205
  Iteration 19: obj = 0.7574825, train = 0.8376926, valid = 0.6678965, test = 0.8704802
  Iteration 20: obj = 0.75723267, train = 0.83707154, valid = 0.66353226, test = 0.8755938
  Iteration 24: obj = 0.75630724, train = 0.83641005, valid = 0.6631685, test = 0.87020046
  Iteration 25: obj = 0.7560841, train = 0.8370162, valid = 0.66222954, test = 0.86887956
  Iteration 26: obj = 0.75597596, train = 0.8367602, valid = 0.66129315, test = 0.8675699
  Iteration 27: obj = 0.75564915, train = 0.83682954, valid = 0.6532021, test = 0.8662714
  Iteration 35: obj = 0.75403625, train = 0.8334681, valid = 0.6519841, test = 0.8567673
  Iteration 37: obj = 0.75357985, train = 0.83140814, valid = 0.65114427, test = 0.8538594
  Iteration 38: obj = 0.75309265, train = 0.83316743, valid = 0.6504916, test = 0.852679
  Iteration 39: obj = 0.75337523, train = 0.8309732, valid = 0.64998746, test = 0.851712
  Iteration 40: obj = 0.753062, train = 0.8307537, valid = 0.64948, test = 0.85098535
  Iteration 41: obj = 0.75285053, train = 0.830533, valid = 0.6489699, test = 0.85026205
  Iteration 42: obj = 0.75240123, train = 0.8303113, valid = 0.6484571, test = 0.84936684
  Iteration 43: obj = 0.7524459, train = 0.8300884, valid = 0.6479417, test = 0.8487526
  Iteration 44: obj = 0.75212497, train = 0.8298642, valid = 0.64742297, test = 0.84867185
  Iteration 45: obj = 0.75187576, train = 0.8296386, valid = 0.64690113, test = 0.8502486
  Iteration 46: obj = 0.7515938, train = 0.82941103, valid = 0.6463746, test = 0.8479715
  Iteration 47: obj = 0.7516179, train = 0.82918215, valid = 0.6458447, test = 0.84815115
  Iteration 48: obj = 0.751276, train = 0.8289511, valid = 0.6453099, test = 0.84939325
  Iteration 49: obj = 0.75113434, train = 0.82871854, valid = 0.6447716, test = 0.8492923
  Iteration 50: obj = 0.75098747, train = 0.82848454, valid = 0.6442296, test = 0.847902
  Iteration 51: obj = 0.750704, train = 0.8282485, valid = 0.6436827, test = 0.8471339
  Iteration 52: obj = 0.7506178, train = 0.8280111, valid = 0.6431327, test = 0.84711725
  Iteration 53: obj = 0.74985296, train = 0.82777417, valid = 0.6425836, test = 0.84678674
  Iteration 54: obj = 0.74995023, train = 0.8275387, valid = 0.6420379, test = 0.8467506
  Iteration 55: obj = 0.74972016, train = 0.82730436, valid = 0.64149463, test = 0.8462545
  Iteration 56: obj = 0.74988467, train = 0.82707065, valid = 0.64095277, test = 0.84298414
  Iteration 57: obj = 0.7493355, train = 0.82628214, valid = 0.6404115, test = 0.8458972
  Iteration 58: obj = 0.7491619, train = 0.8261148, valid = 0.6398709, test = 0.84573156
  Iteration 59: obj = 0.7492572, train = 0.82666016, valid = 0.6393304, test = 0.84677184
  Iteration 60: obj = 0.748651, train = 0.82652634, valid = 0.63718927, test = 0.8458289
  Iteration 63: obj = 0.7480619, train = 0.82648516, valid = 0.6371622, test = 0.8449228
  Iteration 64: obj = 0.7477451, train = 0.8259891, valid = 0.6366159, test = 0.844702
  Iteration 65: obj = 0.7477539, train = 0.8258543, valid = 0.6360699, test = 0.8448211
  Iteration 66: obj = 0.74754304, train = 0.8257188, valid = 0.6355213, test = 0.8434148
  Iteration 67: obj = 0.7472568, train = 0.825582, valid = 0.6333841, test = 0.8431543
  Iteration 68: obj = 0.74696267, train = 0.8254451, valid = 0.6277535, test = 0.84390056
  Iteration 69: obj = 0.74685115, train = 0.8253081, valid = 0.61539674, test = 0.84349847
  Iteration 83: obj = 0.74373525, train = 0.82261527, valid = 0.613894, test = 0.8363949
  Iteration 86: obj = 0.74327695, train = 0.81939733, valid = 0.61294997, test = 0.83506435
  Iteration 87: obj = 0.74322504, train = 0.8190067, valid = 0.61190724, test = 0.8346372
  Iteration 89: obj = 0.7425802, train = 0.81870085, valid = 0.5919547, test = 0.8338034
  Iteration 99: obj = 0.7402284, train = 0.81627953, valid = 0.5908742, test = 0.83000374
  Iteration 104: obj = 0.7396047, train = 0.81584334, valid = 0.5867853, test = 0.8283419
  Iteration 106: obj = 0.7398345, train = 0.8155682, valid = 0.5846882, test = 0.7836593
  Iteration 108: obj = 0.7375331, train = 0.8130157, valid = 0.5671221, test = 0.76781666
  Iteration 113: obj = 0.73755205, train = 0.8173808, valid = 0.5615242, test = 0.80609846
  Iteration 124: obj = 0.73442745, train = 0.81216896, valid = 0.56079954, test = 0.7809559
  Iteration 129: obj = 0.7326534, train = 0.8105207, valid = 0.5606687, test = 0.7537011
  Iteration 131: obj = 0.73387706, train = 0.809675, valid = 0.5589714, test = 0.7616737
  Iteration 132: obj = 0.73314095, train = 0.80986303, valid = 0.55668485, test = 0.74240863
  Iteration 134: obj = 0.73263884, train = 0.8082973, valid = 0.5560804, test = 0.78798836
  Iteration 136: obj = 0.73159546, train = 0.80736256, valid = 0.53668267, test = 0.7492142
  Iteration 154: obj = 0.729536, train = 0.8039262, valid = 0.5342363, test = 0.71127343
  Iteration 155: obj = 0.7274384, train = 0.8013457, valid = 0.5315691, test = 0.7213424
  Iteration 161: obj = 0.725323, train = 0.7920767, valid = 0.52562565, test = 0.7340823
  Iteration 163: obj = 0.72518384, train = 0.79953325, valid = 0.51786804, test = 0.7198529
  Iteration 177: obj = 0.7248829, train = 0.79176664, valid = 0.515952, test = 0.73073626
  Iteration 178: obj = 0.72329843, train = 0.7878206, valid = 0.5114155, test = 0.73292255
  Iteration 180: obj = 0.7244121, train = 0.7884387, valid = 0.50900924, test = 0.71374565
  Iteration 191: obj = 0.7214504, train = 0.7891369, valid = 0.49697143, test = 0.6953857
  Iteration 210: obj = 0.7175903, train = 0.77925134, valid = 0.49433464, test = 0.7086319
  Iteration 227: obj = 0.71601236, train = 0.7755927, valid = 0.49315333, test = 0.690786
  Iteration 230: obj = 0.71479195, train = 0.7773392, valid = 0.47402608, test = 0.703504
  Iteration 247: obj = 0.71136105, train = 0.76977456, valid = 0.4671049, test = 0.7570657
  Iteration 250: obj = 0.71118706, train = 0.76743186, valid = 0.4606103, test = 0.7600636
  Iteration 259: obj = 0.7080637, train = 0.7648523, valid = 0.45440298, test = 0.7417909
  Iteration 261: obj = 0.70812553, train = 0.76291066, valid = 0.45362735, test = 0.69002104
  Iteration 269: obj = 0.70687276, train = 0.75778884, valid = 0.44471726, test = 0.7489511
  Iteration 279: obj = 0.7072627, train = 0.75367296, valid = 0.44370127, test = 0.78717506
  Iteration 296: obj = 0.7037215, train = 0.74568766, valid = 0.4384157, test = 0.75328904
  Iteration 303: obj = 0.70107865, train = 0.74511313, valid = 0.41159678, test = 0.73238945
  Iteration 356: obj = 0.6935912, train = 0.7195407, valid = 0.41016635, test = 0.74088687
  Iteration 368: obj = 0.6923958, train = 0.7196732, valid = 0.40594453, test = 0.76823246
  Iteration 370: obj = 0.6907102, train = 0.7181648, valid = 0.40380278, test = 0.72123575
  Iteration 372: obj = 0.6922037, train = 0.71294534, valid = 0.3959604, test = 0.7575614
  Iteration 377: obj = 0.68898565, train = 0.7136721, valid = 0.38282838, test = 0.7800292
  Iteration 395: obj = 0.68672544, train = 0.7028498, valid = 0.37801903, test = 0.76050866
  Iteration 405: obj = 0.68537295, train = 0.7123771, valid = 0.37777984, test = 0.733293
  Iteration 422: obj = 0.6824563, train = 0.6941775, valid = 0.3722008, test = 0.71997505
  Iteration 431: obj = 0.6782726, train = 0.6873244, valid = 0.3715672, test = 0.7141142
  Iteration 433: obj = 0.6792999, train = 0.6934246, valid = 0.37032795, test = 0.7111799
  Iteration 457: obj = 0.6764448, train = 0.6837513, valid = 0.36989492, test = 0.6963068
  Iteration 459: obj = 0.67482215, train = 0.68348587, valid = 0.36905113, test = 0.72224796
  Iteration 470: obj = 0.6716533, train = 0.67729974, valid = 0.3619641, test = 0.704642
  Iteration 485: obj = 0.6704101, train = 0.67241526, valid = 0.35421365, test = 0.696074
  Iteration 488: obj = 0.67136204, train = 0.6753402, valid = 0.3483554, test = 0.767157
  Iteration 499: obj = 0.6701351, train = 0.66602993, valid = 0.3455974, test = 0.68420345
  Iteration 503: obj = 0.6687491, train = 0.6669698, valid = 0.3432938, test = 0.66178024
  Iteration 517: obj = 0.6663156, train = 0.6625532, valid = 0.33965158, test = 0.6886153
  Iteration 518: obj = 0.66779697, train = 0.65606666, valid = 0.33103293, test = 0.6697074
  Iteration 521: obj = 0.6652857, train = 0.6575815, valid = 0.32919234, test = 0.70180786
  Iteration 527: obj = 0.6644536, train = 0.65172166, valid = 0.31967172, test = 0.6764285
  Iteration 540: obj = 0.66433454, train = 0.6508429, valid = 0.31815016, test = 0.7084147
  Iteration 545: obj = 0.6605763, train = 0.64376545, valid = 0.31243032, test = 0.72906387
  Iteration 556: obj = 0.6615378, train = 0.6490193, valid = 0.30151093, test = 0.71777475
  Iteration 590: obj = 0.6552907, train = 0.63799125, valid = 0.28574416, test = 0.7020271
  Iteration 617: obj = 0.6519022, train = 0.6291973, valid = 0.27932343, test = 0.69989794
  Iteration 637: obj = 0.64862686, train = 0.62009263, valid = 0.27881038, test = 0.6994536
  Iteration 651: obj = 0.6468666, train = 0.62166226, valid = 0.2777496, test = 0.7059349
  Iteration 652: obj = 0.6479695, train = 0.6222819, valid = 0.26859847, test = 0.67529136
  Iteration 667: obj = 0.6438307, train = 0.62023354, valid = 0.26794493, test = 0.6949636
  Iteration 686: obj = 0.6419845, train = 0.62342703, valid = 0.26593834, test = 0.6924201
  Iteration 692: obj = 0.64440876, train = 0.61522347, valid = 0.26032132, test = 0.6873214
  Iteration 694: obj = 0.6442071, train = 0.61962724, valid = 0.24702626, test = 0.68412983
  Iteration 703: obj = 0.6428723, train = 0.6186048, valid = 0.24362698, test = 0.6609248
  Iteration 723: obj = 0.64261913, train = 0.61844575, valid = 0.23853743, test = 0.68484795
  Iteration 740: obj = 0.6382756, train = 0.6052557, valid = 0.23742136, test = 0.6958071
  Iteration 784: obj = 0.63204956, train = 0.6052002, valid = 0.23420385, test = 0.69504356
  Iteration 842: obj = 0.6306746, train = 0.6030507, valid = 0.22987372, test = 0.6460124
  Iteration 862: obj = 0.6308618, train = 0.6003814, valid = 0.22954047, test = 0.6784371
  Iteration 886: obj = 0.6258846, train = 0.6046735, valid = 0.22183883, test = 0.65851223
  Iteration 1000: obj = 0.6172606, train = 0.59598184, valid = 0.2549007, test = 0.6478243
  Iteration 1043: obj = 0.6083075, train = 0.58164763, valid = 0.21788913, test = 0.66527474
  Iteration 2000: obj = 0.54820275, train = 0.48161727, valid = 0.29577044, test = 0.39806664
(22, 47, 1)
MEM error = 1.0, GRU error = 0.665/0.66527474, better = 9/11
overall MEM error   = 0.616
overall GRU error = 0.502

def plotOne(feat, y, yMEM, predsMEM, X):
    MyMEM = PE(y, yMEM)
    MpredsMEM = PE(y, predsMEM)
    plt.figure(figsize=(16,6))
    plt.scatter(X, y, label='Concentration')
    plt.plot(X, yMEM, label='Mixed Effects Model (MSE=' + str(MyMEM)[:5] + ')')
    plt.plot(X, predsMEM, label='GRU, with V/CL features (MSE=' + str(MpredsMEM)[:5] + ')')
    plt.xlabel("Time")
    plt.ylabel("Concentration (predicted and observed)")
    plt.legend()
    plt.xlim(1,47)
    plt.ylim(0,30)
    plt.show()
    plt.figure(figsize=(16,6))
    plt.plot(X, [0]*len(X), 'k--')
    plt.plot(X, list(y - yMEM), label="Residuals, MEM")
    plt.plot(X, list(y - numpy.reshape(predsMEM, [-1])), label="Residuals, GRU with V/CL features")
    plt.xlabel("Time")
    plt.ylabel("Residual")
    plt.legend()
    plt.xlim(1,47)
    plt.show()

memOverallErr = PE(yyMEM, yy)

print(memOverallErr)

0.58892334

# Some quick plots

plotOne(XX[-2], yy[-2], yyMEM[-2], predictionVec[-2], timeVecs[-2])

plotOne(XX[-1], yy[-1], yyMEM[-1], predictionVec[-1], timeVecs[-1])

PEmem = numpy.nan_to_num((yy - yyMEM)/yyMEM)
PEgru = numpy.nan_to_num((yy - predictionVec)/(predictionVec))

biasMEM = numpy.median(PEmem)
biasGRU = numpy.median(PEgru)

# Compare the models in terms of biases
(biasMEM, biasGRU)

(-0.22123356, -0.30846107)

errorMEM = numpy.median(numpy.abs(PEmem))
errorGRU = numpy.median(numpy.abs(PEgru))

(errorMEM, errorGRU)

(0.58892334, 0.49743247)

# Quick sanity check re. overall accuracy of the models
PE(yyMEM, yy)

0.58892334

PE(predictionVec, yy)

0.49743247