# Path: data_loader.py
# def load_syntetic(dataset):
#     # fast loader
#     filename = base + 'syntetic/features%d.npz' % dataset
#     npz = np.load(filename)
#     # add superpixel area, which is one because pixel == superpixel in this dataset
#     Y = [np.vstack([y, np.ones(y.size)]).T for y in list(npz['Y'])]
#     X = list(npz['X'])
#     return X, Y
# 
# Path: data_loader.py
# def load_msrc_hdf(filename):
#     # hdf5 loader, the best
#     f = h5py.File(filename, 'r', libver='latest')
#     g_u = f['features']['unaries']
#     data = np.empty(g_u['data'].shape, dtype=np.float64)
#     indices = np.empty(g_u['indices'].shape, dtype=np.int32)
#     indptr = np.empty(g_u['indptr'].shape, dtype=np.int32)
#     g_u['data'].read_direct(data)
#     g_u['indices'].read_direct(indices)
#     g_u['indptr'].read_direct(indptr)
#     width = g_u.attrs['width']
#     height = g_u.attrs['height']
#     raw_unaries = sps.csr_matrix((data, indices, indptr), shape=(height, width))
#     del data
#     del indices
#     del indptr
# 
#     g_f = f['features']
#     raw_labels = np.empty(g_f['labels'].shape, dtype=np.int32)
#     g_f['labels'].read_direct(raw_labels)
#     raw_areas = np.empty(g_f['areas'].shape, dtype=np.int32)
#     g_f['areas'].read_direct(raw_areas)
# 
#     raw_labels_all = np.vstack((raw_labels, raw_areas)).T
# 
#     spnum = np.empty(g_f['spnum'].shape, dtype=np.int32)
#     g_f['spnum'].read_direct(spnum)
#     unaries = []
#     labels = []
#     pos = 0
#     for n in spnum:
#         unaries.append(raw_unaries[pos:pos+n, :])
#         labels.append(raw_labels_all[pos:pos+n, :])
#         pos += n
#     del raw_unaries
#     del raw_labels
#     del raw_labels_all
#     del raw_areas
# 
#     edges_num = np.empty(g_f['edges_num'].shape, dtype=np.int32)
#     g_f['edges_num'].read_direct(edges_num)
#     raw_edges = np.empty(g_f['edges'].shape, dtype=np.int32)
#     g_f['edges'].read_direct(raw_edges)
#     raw_pairwise = np.empty(g_f['pairwise'].shape, dtype=np.float64)
#     g_f['pairwise'].read_direct(raw_pairwise)
# 
#     edges = []
#     pairwise = []
# 
#     pos = 0
#     for n in edges_num:
#         edges.append(raw_edges[pos:pos+n, :])
#         pairwise.append(raw_pairwise[pos:pos+n, :])
#         pos += n
#     del raw_edges
#     del raw_pairwise
# 
#     X = zip(unaries, edges, pairwise)
#     Y = labels
# 
#     Xtrain = X[:276]
#     Ytrain = Y[:276]
#     Xtest = X[276:]
#     Ytest = Y[276:]
# 
#     f.close()
# 
#     return Xtrain, Ytrain, Xtest, Ytest
# 
# def load_msrc_weak_train_mask(filename, n):
#     f = h5py.File(filename, 'r', libver='latest')
#     name = 'train_mask_%d' % n
#     mask = np.empty(f[name].shape, dtype=np.int32)
#     f[name].read_direct(mask)
#     f.close()
#     return mask
# 
# Path: label.py
# class Label(object):
#     def __init__(self, full, weak, weights, full_labeled, relaxed=False):
#         self.full = full
#         self.weak = weak
#         self.weights = weights
#         self.full_labeled = full_labeled
#         self.relaxed = relaxed
#         if not self.relaxed:
#             if self.full is None and self.weak is None:
#                 raise ValueError("You should specify full or weak labels")
#             if self.weak is None:
#                 self.weak = np.unique(self.full).astype(np.int32)
#             if self.full is None:
#                 self.full = np.random.choice(self.weak, self.weights.size).astype(np.int32)
# 
#     def __eq__(self, other):
#         if self.relaxed or other.relaxed:
#             return np.all(self.full[0] == other.full[0]) \
#                 and np.all(self.full[1] == other.full[1])
#         return np.all(self.full == other.full)


import numpy as np
from data_loader import load_syntetic as load_syntetic_dataset
from data_loader import load_msrc_hdf, load_msrc_weak_train_mask
from label import Label

    Xnew = []
    Ynew = []
    for x, y in zip(X, Y):
        Xnew.append((x[0], x[1], np.ones((x[2].shape[0], 1))))
        y_ = y[:, 0].astype(np.int32)
        labels = np.unique(y_)
        y[y_ == labels[0], 0] = 0
        for l in labels[1:]:
            if l == 0:
                continue
            y[y_ == l, 0] = 1
        Ynew.append(y)

    X = Xnew
    Y = Ynew

    x_train = X[:n_train]
    y_train = [Label(y[:, 0].astype(np.int32), None, y[:, 1], True)
               for y in Y[:n_train]]

    x_test = X[n_train:]
    y_test = [Label(y[:, 0].astype(np.int32), None, y[:, 1], True)
              for y in Y[n_train:]]

    return x_train, y_train, x_test, y_test


def load_syntetic(dataset, n_full, n_train):
    # splitting syntetic dataset

# Path: data_loader.py
# def load_syntetic(dataset):
#     # fast loader
#     filename = base + 'syntetic/features%d.npz' % dataset
#     npz = np.load(filename)
#     # add superpixel area, which is one because pixel == superpixel in this dataset
#     Y = [np.vstack([y, np.ones(y.size)]).T for y in list(npz['Y'])]
#     X = list(npz['X'])
#     return X, Y
# 
# Path: data_loader.py
# def load_msrc_hdf(filename):
#     # hdf5 loader, the best
#     f = h5py.File(filename, 'r', libver='latest')
#     g_u = f['features']['unaries']
#     data = np.empty(g_u['data'].shape, dtype=np.float64)
#     indices = np.empty(g_u['indices'].shape, dtype=np.int32)
#     indptr = np.empty(g_u['indptr'].shape, dtype=np.int32)
#     g_u['data'].read_direct(data)
#     g_u['indices'].read_direct(indices)
#     g_u['indptr'].read_direct(indptr)
#     width = g_u.attrs['width']
#     height = g_u.attrs['height']
#     raw_unaries = sps.csr_matrix((data, indices, indptr), shape=(height, width))
#     del data
#     del indices
#     del indptr
# 
#     g_f = f['features']
#     raw_labels = np.empty(g_f['labels'].shape, dtype=np.int32)
#     g_f['labels'].read_direct(raw_labels)
#     raw_areas = np.empty(g_f['areas'].shape, dtype=np.int32)
#     g_f['areas'].read_direct(raw_areas)
# 
#     raw_labels_all = np.vstack((raw_labels, raw_areas)).T
# 
#     spnum = np.empty(g_f['spnum'].shape, dtype=np.int32)
#     g_f['spnum'].read_direct(spnum)
#     unaries = []
#     labels = []
#     pos = 0
#     for n in spnum:
#         unaries.append(raw_unaries[pos:pos+n, :])
#         labels.append(raw_labels_all[pos:pos+n, :])
#         pos += n
#     del raw_unaries
#     del raw_labels
#     del raw_labels_all
#     del raw_areas
# 
#     edges_num = np.empty(g_f['edges_num'].shape, dtype=np.int32)
#     g_f['edges_num'].read_direct(edges_num)
#     raw_edges = np.empty(g_f['edges'].shape, dtype=np.int32)
#     g_f['edges'].read_direct(raw_edges)
#     raw_pairwise = np.empty(g_f['pairwise'].shape, dtype=np.float64)
#     g_f['pairwise'].read_direct(raw_pairwise)
# 
#     edges = []
#     pairwise = []
# 
#     pos = 0
#     for n in edges_num:
#         edges.append(raw_edges[pos:pos+n, :])
#         pairwise.append(raw_pairwise[pos:pos+n, :])
#         pos += n
#     del raw_edges
#     del raw_pairwise
# 
#     X = zip(unaries, edges, pairwise)
#     Y = labels
# 
#     Xtrain = X[:276]
#     Ytrain = Y[:276]
#     Xtest = X[276:]
#     Ytest = Y[276:]
# 
#     f.close()
# 
#     return Xtrain, Ytrain, Xtest, Ytest
# 
# def load_msrc_weak_train_mask(filename, n):
#     f = h5py.File(filename, 'r', libver='latest')
#     name = 'train_mask_%d' % n
#     mask = np.empty(f[name].shape, dtype=np.int32)
#     f[name].read_direct(mask)
#     f.close()
#     return mask
# 
# Path: label.py
# class Label(object):
#     def __init__(self, full, weak, weights, full_labeled, relaxed=False):
#         self.full = full
#         self.weak = weak
#         self.weights = weights
#         self.full_labeled = full_labeled
#         self.relaxed = relaxed
#         if not self.relaxed:
#             if self.full is None and self.weak is None:
#                 raise ValueError("You should specify full or weak labels")
#             if self.weak is None:
#                 self.weak = np.unique(self.full).astype(np.int32)
#             if self.full is None:
#                 self.full = np.random.choice(self.weak, self.weights.size).astype(np.int32)
# 
#     def __eq__(self, other):
#         if self.relaxed or other.relaxed:
#             return np.all(self.full[0] == other.full[0]) \
#                 and np.all(self.full[1] == other.full[1])
#         return np.all(self.full == other.full)


# Path: utils.py
import numpy as np
from data_loader import load_syntetic as load_syntetic_dataset
from data_loader import load_msrc_hdf, load_msrc_weak_train_mask
from label import Label


    Xnew = []
    Ynew = []
    for x, y in zip(X, Y):
        Xnew.append((x[0], x[1], np.ones((x[2].shape[0], 1))))
        y_ = y[:, 0].astype(np.int32)
        labels = np.unique(y_)
        y[y_ == labels[0], 0] = 0
        for l in labels[1:]:
            if l == 0:
                continue
            y[y_ == l, 0] = 1
        Ynew.append(y)

    X = Xnew
    Y = Ynew

    x_train = X[:n_train]
    y_train = [Label(y[:, 0].astype(np.int32), None, y[:, 1], True)
               for y in Y[:n_train]]

    x_test = X[n_train:]
    y_test = [Label(y[:, 0].astype(np.int32), None, y[:, 1], True)
              for y in Y[n_train:]]

    return x_train, y_train, x_test, y_test


def load_syntetic(dataset, n_full, n_train):
    # splitting syntetic dataset

    X, Y = load_syntetic_dataset(dataset)