# Path: geepee/utils.py
# def unflatten_dict(params, params_args):
#     keys, indices, shapes = params_args[0], params_args[1], params_args[2]
#     vals = np.split(params, indices)
#     params_dict = {}
#     for i, key in enumerate(sorted(keys)):
#         params_dict[key] = np.reshape(vals[i], shapes[key])
#     return params_dict


import sys
import geepee.vfe_models as vfe
import math
import numpy as np
import time
import argparse
import os
from geepee.utils import unflatten_dict
from metrics import *
for i in test_iters:
    outname1 = output_path + name + '_vfe_' + str(M) + '_iter_' + str(i) +'.error'
    if not os.path.exists(os.path.dirname(outname1)):
        os.makedirs(os.path.dirname(outname1))
    outfile1 = open(outname1, 'w')
    outname2 = output_path + name + '_vfe_' + str(M) + '_iter_' + str(i) + '.nll'
    outfile2 = open(outname2, 'w')
    outfile1s.append(outfile1)
    outfile2s.append(outfile2)
outname3 = output_path + name + '_vfe_' + str(M) + '.time'
outfile3 = open(outname3, 'w')

for i in range(nosplits):
    print 'running split', i

    train_ind_file = datapath + 'index_train_' + str(i) + '.txt'
    test_ind_file = datapath + 'index_test_' + str(i) + '.txt'
    index_train = np.loadtxt(train_ind_file, dtype=np.int32)
    index_test = np.loadtxt(test_ind_file, dtype=np.int32)
    X_train = X[index_train, :]
    y_train = y[index_train, :]
    X_test = X[index_test, :]
    y_test = y[index_test, :]
    y_test = y_test[:, 0]

    if normalise_x:
        # we normalise the train input
        std_X_train = np.std(X_train, 0)
        std_X_train[ std_X_train == 0 ] = 1
        mean_X_train = np.mean(X_train, 0)

# Path: geepee/utils.py
# def unflatten_dict(params, params_args):
#     keys, indices, shapes = params_args[0], params_args[1], params_args[2]
#     vals = np.split(params, indices)
#     params_dict = {}
#     for i, key in enumerate(sorted(keys)):
#         params_dict[key] = np.reshape(vals[i], shapes[key])
#     return params_dict


# Path: exps/gpc/vfe/run_sparse_cla.py
import sys
import geepee.vfe_models as vfe
import math
import numpy as np
import time
import argparse
import os
from geepee.utils import unflatten_dict
from metrics import *

for i in test_iters:
    outname1 = output_path + name + '_vfe_' + str(M) + '_iter_' + str(i) +'.error'
    if not os.path.exists(os.path.dirname(outname1)):
        os.makedirs(os.path.dirname(outname1))
    outfile1 = open(outname1, 'w')
    outname2 = output_path + name + '_vfe_' + str(M) + '_iter_' + str(i) + '.nll'
    outfile2 = open(outname2, 'w')
    outfile1s.append(outfile1)
    outfile2s.append(outfile2)
outname3 = output_path + name + '_vfe_' + str(M) + '.time'
outfile3 = open(outname3, 'w')

for i in range(nosplits):
    print 'running split', i

    train_ind_file = datapath + 'index_train_' + str(i) + '.txt'
    test_ind_file = datapath + 'index_test_' + str(i) + '.txt'
    index_train = np.loadtxt(train_ind_file, dtype=np.int32)
    index_test = np.loadtxt(test_ind_file, dtype=np.int32)
    X_train = X[index_train, :]
    y_train = y[index_train, :]
    X_test = X[index_test, :]
    y_test = y[index_test, :]
    y_test = y_test[:, 0]

    if normalise_x:
        # we normalise the train input
        std_X_train = np.std(X_train, 0)
        std_X_train[ std_X_train == 0 ] = 1
        mean_X_train = np.mean(X_train, 0)

        X_train = (X_train - mean_X_train) / std_X_train