def get_binary_labeled_data():
    labeled_data = []

    x = [
        [-0.155, 0.128, -0.688, 0.028, -0.555, -1.112, 0.427, -0.199, 0.807, -1.705],
        [-0.847, 2.342, 1.156, 0.767, 0.171, 0.814, -0.657, 0.631, -0.346, 0.682],
        [-0.886, 1.638, -0.818, -0.838, 1.137, 2.086, -0.662, 1.396, -1.613, -0.268],
        [-0.002, -0.569, -1.079, -0.667, 0.928, 0.548, 0.75, 0.427, -1.079, 0.676],
        [-0.772, -2.468, 1.688, 0.009, -1.934, -1.657, -0.987, -0.162, 2.354, 0.337],
        [-0.077, 0.2, 0.244, 0.583, -0.458, -2.076, 1.646, 1.081, 0.905, -0.213],
        [0.676, 0.298, 0.222, 1.152, -0.911, -0.959, -0.266, 2.107, 1.146, -1.337],
        [0.02, 0.749, -1.476, -0.093, 0.143, -0.084, 0.303, 0.53, -0.131, -1.536],
        [0.336, -1.074, -2.36, -0.445, 0.222, 1.248, -1.067, 0.595, -0.489, -1.463],
        [-1.145, -0.653, 0.207, 1.499, 3.571, 2.755, 2.161, 0.935, -4.284, -0.501],
        [-0.328, -0.903, -0.243, -0.55, -0.033, 0.74, -0.874, 0.945, -0.118, -0.651],
        [0.914, -0.485, 1.077, -0.525, -0.495, -0.129, -0.582, -0.089, 0.542, 0.965],
        [0.38, 1.024, 0.351, 0.11, 0.506, 1.275, -0.681, -0.129, -0.789, -0.011],
        [0.429, -0.502, -0.601, 0.026, 0.169, 0.673, -0.504, -1.534, -0.314, 0.147],
        [-0.891, 1.432, -0.523, 0.906, 2.416, 1.923, 1.397, -0.033, -2.911, 0.369],
        [-0.539, 0.694, 1.398, 0.791, 2.225, 1.792, 1.263, 2.338, -2.685, -0.125],
        [0.608, 0.182, 1.06, 0.361, 2.031, 0.612, 2.296, 1.488, -2.239, 0.997],
        [-0.905, -0.842, -0.365, 0.443, -2.235, -1.46, -1.648, 2.599, 2.626, 0.466],
        [0.918, -0.203, -1.272, -1.751, 0.733, 0.491, 0.527, 2.606, -0.864, 0.038],
        [0.305, 0.417, -1.299, 2.556, 0.791, 0.525, 0.574, 1.107, -0.931, 0.785],
        [-0.715, 0.499, -0.663, -0.145, -0.359, -2.104, 1.821, 1.394, 0.808, -0.074],
        [1.444, 0.863, 1.106, 1.037, -3.81, -3.141, -2.081, -0.112, 4.612, 0.458],
        [0.096, 1.174, 0.332, -0.205, 1.645, 1.924, 0.264, -0.302, -2.108, -0.34],
        [0.087, -0.307, 0.169, 0.908, 0.013, 0.118, -0.113, -2.355, -0.038, -0.815],
        [1.778, -1.174, 0.187, 1.413, 0.86, 0.457, 0.752, 2.357, -0.989, -0.069],
        [0.615, -0.899, -1.668, -0.412, -2.519, -2.281, -1.147, 1.365, 3.091, 0.266],
        [0.187, -1.107, 1.076, -0.821, 0.237, 0.819, -0.566, 1.707, -0.416, 1.224],
        [-0.623, -0.78, 0.93, 1.717, 1.294, 0.872, 0.924, 0.723, -1.526, -0.134],
        [0.547, -0.759, -0.056, -0.96, -0.757, -0.974, -0.024, 1.359, 0.989, -0.579],
        [1.526, -0.566, -1.294, 0.304, 2.391, 1.91, 1.374, 0.88, -2.882, 0.843],
        [0.99, -1.163, 1.838, 2.009, -1.477, -0.983, -1.069, 1.438, 1.739, 2.695],
        [0.259, 1.319, -1.202, 1.143, -0.586, 0.457, -1.37, 1.26, 0.515, -0.124],
        [-0.652, -2.016, 1.123, -0.41, -0.876, -0.521, -0.703, 0.05, 1.019, 0.476],
        [0.482, 0.986, 0.928, -0.976, -0.655, -1.643, 0.873, 0.966, 1.021, 2.519],
        [1.905, 0.119, 0.527, -0.333, 4.131, 3.402, 2.262, -0.386, -5.0, 0.432],
        [0.168, 0.64, 0.492, 1.673, 0.817, -0.734, 2.017, 0.229, -0.698, 0.697],
        [-0.206, -0.653, 0.784, 0.04, 2.463, 1.989, 1.393, -0.536, -2.973, -0.461],
        [-1.084, 0.681, -1.377, -1.338, 0.453, 1.236, -0.716, -1.948, -0.726, -0.501],
        [0.882, -2.013, 1.037, -0.133, 0.792, 1.467, -0.476, -1.637, -1.127, -0.045],
        [-0.833, 2.264, -0.509, 0.213, -0.343, 0.163, -0.685, -1.036, 0.323, -1.17],
        [1.233, 2.281, -2.832, 0.348, -0.643, -0.616, -0.255, -2.365, 0.796, -0.979],
        [0.48, 0.48, 2.151, -0.025, -0.041, 0.912, -1.078, 0.338, -0.145, 1.146],
        [-1.217, -0.075, 1.407, 0.097, -0.53, 0.067, -0.852, -0.134, 0.537, 0.354],
        [1.225, 0.748, 0.64, 1.665, 0.56, 1.822, -1.212, 1.652, -0.959, -0.11],
        [-0.605, -1.807, -0.787, 0.103, -1.448, -0.455, -1.616, 0.872, 1.601, -2.656],
        [0.599, -0.337, 0.846, -0.245, -0.709, 0.179, -1.239, -2.055, 0.701, 0.244],
        [2.425, 1.856, -0.801, 0.909, -0.206, -0.704, 0.484, -0.77, 0.359, -0.755],
        [-0.305, -1.108, 0.678, -1.157, -0.694, -0.363, -0.613, -0.865, 0.797, 0.013],
        [-0.677, -0.6, 0.299, 0.759, -0.754, -0.873, -0.131, -0.043, 0.965, 0.213],
        [-0.46, 1.3, 0.071, 0.057, 0.835, 0.44, 0.735, 0.344, -0.96, 2.074],
        [1.477, -1.096, 1.185, 0.157, 0.067, -1.351, 1.605, 0.354, 0.21, -0.494],
        [0.686, -0.897, 1.354, 0.068, -2.752, -1.716, -2.121, -0.386, 3.217, 1.67],
        [-1.223, -1.098, 0.151, 0.921, 2.474, 1.957, 1.444, -1.737, -2.978, -3.081],
        [0.032, -0.826, -0.407, -0.206, 2.068, 1.392, 1.479, 0.384, -2.438, -0.581],
        [1.089, 0.862, -3.557, 0.4, -0.083, -1.598, 1.662, 0.064, 0.416, -0.762],
        [-1.72, 0.213, -1.292, -0.796, -1.166, -0.744, -0.88, -2.927, 1.366, 0.997],
        [0.208, -1.308, -0.453, 0.229, 2.416, 1.798, 1.536, 0.364, -2.884, 0.593],
        [0.778, 0.088, -0.372, 0.841, 2.304, 1.824, 1.342, 0.739, -2.773, -1.642],
        [-0.618, -0.112, -0.001, -1.319, 2.11, 1.534, 1.383, -0.73, -2.512, 1.284],
        [0.282, 1.513, -0.806, 1.486, -0.067, 0.864, -1.063, -0.529, -0.109, 0.578],
        [0.025, 0.846, 0.145, 1.118, 3.226, 2.494, 1.948, -0.484, -3.871, -0.404],
        [0.073, 1.199, 1.08, 0.151, 1.161, 0.109, 1.581, -1.265, -1.23, 1.069],
        [0.493, -0.777, -0.979, 0.488, 0.456, 2.217, -1.806, 1.192, -0.932, 1.577],
        [-0.227, 0.431, 0.739, -0.286, -0.379, -0.643, 0.162, -0.499, 0.527, -1.899],
        [-0.074, 0.419, 1.523, -1.294, -0.195, -0.913, 0.733, -0.627, 0.391, 0.654],
        [-0.638, -0.526, -0.505, 3.381, -1.62, -0.805, -1.476, -0.651, 1.851, 1.139],
        [-0.02, 0.72, -0.712, -0.34, 1.873, 1.345, 1.246, 0.966, -2.226, -0.741],
        [1.427, -0.991, -1.295, 0.829, -0.363, -1.303, 0.922, 0.301, 0.647, -1.808],
        [1.192, -0.089, -0.33, 0.173, -1.097, -0.816, -0.698, -1.426, 1.31, -1.074],
        [-0.07, -2.022, -0.998, 0.697, 0.276, 1.19, -0.923, 1.061, -0.533, -0.507],
        [2.173, 0.477, 0.392, 1.075, -1.228, -0.501, -1.243, 0.373, 1.381, -0.117],
        [-0.919, -2.287, -1.167, -0.676, -0.878, -0.055, -1.227, -1.088, 0.925, -1.436],
        [0.064, 0.695, 0.981, -2.103, -0.026, 0.562, -0.666, 0.917, -0.089, 0.959],
        [0.157, -0.064, 1.642, -0.791, -0.085, 0.684, -0.889, 0.059, -0.052, -1.156],
        [1.123, 2.923, 1.073, 0.211, -0.183, 0.927, -1.302, -1.607, -0.001, 1.292],
        [0.292, 0.495, -1.278, 1.736, -0.0, 1.084, -1.211, -0.249, -0.223, 0.673],
        [-0.73, 1.998, -0.376, 2.397, -0.158, -0.291, 0.092, 1.078, 0.225, -0.564],
        [0.431, -0.865, 1.168, 0.7, 0.016, 1.198, -1.314, 0.518, -0.264, -0.355],
        [1.537, 1.249, -0.82, 0.244, -0.85, -0.287, -0.926, 0.348, 0.944, 0.331],
        [0.889, 0.172, -0.004, 0.005, -1.45, -1.093, -0.906, -1.71, 1.734, 0.75],
        [0.223, -0.535, -0.945, 0.316, -0.117, -0.334, 0.201, -0.438, 0.19, -0.398],
        [-0.454, -0.16, -0.665, 0.708, -0.556, -0.6, -0.147, -1.476, 0.703, -2.428],
        [0.658, -1.318, 1.47, -1.013, 2.123, 1.512, 1.427, 1.704, -2.521, -0.397],
        [-0.78, 0.503, 0.09, 1.68, 0.055, 0.134, -0.069, 0.292, -0.085, -0.247],
        [0.666, -1.186, -0.331, -1.419, -1.131, -1.357, -0.144, -0.527, 1.457, 1.875],
        [-1.14, -0.108, -0.267, -0.248, 0.559, 1.292, -0.623, 2.1, -0.848, -0.788],
        [-0.778, -0.107, 2.524, 0.695, 2.038, 1.612, 1.19, 0.66, -2.453, -1.055],
        [1.531, 0.014, 1.64, 0.591, 0.291, 1.326, -1.054, -0.161, -0.576, -1.252],
        [0.202, -1.0, -1.115, -0.408, 0.104, -1.493, 1.819, -1.136, 0.2, -0.149],
        [1.588, -4.399, 0.023, 0.704, 0.184, -0.638, 0.982, 1.83, -0.06, 0.351],
        [0.209, -0.617, -0.38, 0.314, -0.356, -1.659, 1.33, -1.175, 0.712, -0.357],
        [0.988, -1.087, 0.207, 2.328, -0.561, -2.589, 2.067, 0.852, 1.118, 0.488],
        [-0.025, -0.087, 1.763, -0.12, 2.581, 2.167, 1.366, 0.192, -3.132, 0.493],
        [-0.552, -0.789, -0.245, -1.097, 0.233, 0.96, -0.729, -1.921, -0.441, -0.367],
        [-0.221, 0.928, -0.015, 0.342, 0.138, 1.087, -1.011, 0.211, -0.368, -0.755],
        [-0.245, -0.147, -1.008, 0.66, -3.046, -2.052, -2.177, -0.613, 3.593, 1.26],
        [-0.584, 0.212, 1.198, 1.516, 0.479, 1.081, -0.504, -0.944, -0.721, 0.158],
        [0.544, -1.369, 0.3, 0.04, -1.953, -1.411, -1.289, 0.41, 2.323, 0.056],
        [0.455, 1.246, -0.568, -1.183, -1.124, -0.608, -0.97, 0.281, 1.295, -1.347],
        [-0.979, 0.715, 0.128, -0.771, -0.162, -1.105, 0.996, -0.684, 0.397, 0.276]
    ]

    y = [
        1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1,
        1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0,
        0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1
    ]

    feature_names = ['feature%d' % i for i in range(10)]

    for i, label in enumerate(y):
        features = x[i]
        data = {feature_names[i]: value for i, value in enumerate(features)}
        if label:
            label = 'True'
        else:
            label = 'False'

        labeled_data.append({'features': data, 'label': label})

    labeled_data = {'training_data': labeled_data}

    return labeled_data