module MachineLearning.DataSets
(
  dataset1
  , dataset2
)

where

import Numeric.LinearAlgebra.Data (matrix)

dataset1 = matrix 3
  [
    2104, 3, 399900,
    1600, 3, 329900,
    2400, 3, 369000,
    1416, 2, 232000,
    3000, 4, 539900,
    1985, 4, 299900,
    1534, 3, 314900,
    1427, 3, 198999,
    1380, 3, 212000,
    1494, 3, 242500,
    1940, 4, 239999,
    2000, 3, 347000,
    1890, 3, 329999,
    4478, 5, 699900,
    1268, 3, 259900,
    2300, 4, 449900,
    1320, 2, 299900,
    1236, 3, 199900,
    2609, 4, 499998,
    3031, 4, 599000,
    1767, 3, 252900,
    1888, 2, 255000,
    1604, 3, 242900,
    1962, 4, 259900,
    3890, 3, 573900,
    1100, 3, 249900,
    1458, 3, 464500,
    2526, 3, 469000,
    2200, 3, 475000,
    2637, 3, 299900,
    1839, 2, 349900,
    1000, 1, 169900,
    2040, 4, 314900,
    3137, 3, 579900,
    1811, 4, 285900,
    1437, 3, 249900,
    1239, 3, 229900,
    2132, 4, 345000,
    4215, 4, 549000,
    2162, 4, 287000,
    1664, 2, 368500,
    2238, 3, 329900,
    2567, 4, 314000,
    1200, 3, 299000,
    852, 2, 179900,
    1852, 4, 299900,
    1203, 3, 239500
  ]

dataset2 = matrix 3
  [
    0.051267, 0.69956, 1,
    -0.092742, 0.68494, 1,
    -0.21371, 0.69225, 1,
    -0.375, 0.50219, 1,
    -0.51325, 0.46564, 1,
    -0.52477, 0.2098, 1,
    -0.39804, 0.034357, 1,
    -0.30588, -0.19225, 1,
    0.016705, -0.40424, 1,
    0.13191, -0.51389, 1,
    0.38537, -0.56506, 1,
    0.52938, -0.5212, 1,
    0.63882, -0.24342, 1,
    0.73675, -0.18494, 1,
    0.54666, 0.48757, 1,
    0.322, 0.5826, 1,
    0.16647, 0.53874, 1,
    -0.046659, 0.81652, 1,
    -0.17339, 0.69956, 1,
    -0.47869, 0.63377, 1,
    -0.60541, 0.59722, 1,
    -0.62846, 0.33406, 1,
    -0.59389, 0.005117, 1,
    -0.42108, -0.27266, 1,
    -0.11578, -0.39693, 1,
    0.20104, -0.60161, 1,
    0.46601, -0.53582, 1,
    0.67339, -0.53582, 1,
    -0.13882, 0.54605, 1,
    -0.29435, 0.77997, 1,
    -0.26555, 0.96272, 1,
    -0.16187, 0.8019, 1,
    -0.17339, 0.64839, 1,
    -0.28283, 0.47295, 1,
    -0.36348, 0.31213, 1,
    -0.30012, 0.027047, 1,
    -0.23675, -0.21418, 1,
    -0.06394, -0.18494, 1,
    0.062788, -0.16301, 1,
    0.22984, -0.41155, 1,
    0.2932, -0.2288, 1,
    0.48329, -0.18494, 1,
    0.64459, -0.14108, 1,
    0.46025, 0.012427, 1,
    0.6273, 0.15863, 1,
    0.57546, 0.26827, 1,
    0.72523, 0.44371, 1,
    0.22408, 0.52412, 1,
    0.44297, 0.67032, 1,
    0.322, 0.69225, 1,
    0.13767, 0.57529, 1,
    -0.0063364, 0.39985, 1,
    -0.092742, 0.55336, 1,
    -0.20795, 0.35599, 1,
    -0.20795, 0.17325, 1,
    -0.43836, 0.21711, 1,
    -0.21947, -0.016813, 1,
    -0.13882, -0.27266, 1,
    0.18376, 0.93348, 0,
    0.22408, 0.77997, 0,
    0.29896, 0.61915, 0,
    0.50634, 0.75804, 0,
    0.61578, 0.7288, 0,
    0.60426, 0.59722, 0,
    0.76555, 0.50219, 0,
    0.92684, 0.3633, 0,
    0.82316, 0.27558, 0,
    0.96141, 0.085526, 0,
    0.93836, 0.012427, 0,
    0.86348, -0.082602, 0,
    0.89804, -0.20687, 0,
    0.85196, -0.36769, 0,
    0.82892, -0.5212, 0,
    0.79435, -0.55775, 0,
    0.59274, -0.7405, 0,
    0.51786, -0.5943, 0,
    0.46601, -0.41886, 0,
    0.35081, -0.57968, 0,
    0.28744, -0.76974, 0,
    0.085829, -0.75512, 0,
    0.14919, -0.57968, 0,
    -0.13306, -0.4481, 0,
    -0.40956, -0.41155, 0,
    -0.39228, -0.25804, 0,
    -0.74366, -0.25804, 0,
    -0.69758, 0.041667, 0,
    -0.75518, 0.2902, 0,
    -0.69758, 0.68494, 0,
    -0.4038, 0.70687, 0,
    -0.38076, 0.91886, 0,
    -0.50749, 0.90424, 0,
    -0.54781, 0.70687, 0,
    0.10311, 0.77997, 0,
    0.057028, 0.91886, 0,
    -0.10426, 0.99196, 0,
    -0.081221, 1.1089, 0,
    0.28744, 1.087, 0,
    0.39689, 0.82383, 0,
    0.63882, 0.88962, 0,
    0.82316, 0.66301, 0,
    0.67339, 0.64108, 0,
    1.0709, 0.10015, 0,
    -0.046659, -0.57968, 0,
    -0.23675, -0.63816, 0,
    -0.15035, -0.36769, 0,
    -0.49021, -0.3019, 0,
    -0.46717, -0.13377, 0,
    -0.28859, -0.060673, 0,
    -0.61118, -0.067982, 0,
    -0.66302, -0.21418, 0,
    -0.59965, -0.41886, 0,
    -0.72638, -0.082602, 0,
    -0.83007, 0.31213, 0,
    -0.72062, 0.53874, 0,
    -0.59389, 0.49488, 0,
    -0.48445, 0.99927, 0,
    -0.0063364, 0.99927, 0,
    0.63265, -0.030612, 0
    ]