hello-networkx.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment #1\n",
      "score: 7\n",
      "commands: RRDD\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAHVCAYAAADLvzPyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f285ea95cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment #2\n",
      "score: 66\n",
      "commands: DDDRRDRRRD\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd0AAAHVCAYAAAC0biEDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXcYYY8xO/j9n3ElLIllIdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f28766c2da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment #3\n",
      "score: 55555\n",
      "commands: RRRDDDDRDRDDDRDRDRDRRRDDDDDDRRDRDDRRDDDDDRRRDRRDDRDRDDRRDRRRDRRRDRDDRRRDRRDRRDRDRRDDDDRDDRDDRDRRRR\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAHVCAYAAAAgiIjxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f285eaa4f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import json\n",
    "\n",
    "TEST_DATA_1 = json.loads(\"\"\"\n",
    "[[293, 471, 409, 485, 294, 463, 256, 531, 416, 259, 496, 29, 706, 767, 390, 28, 550, 746, 142, 465, 458, 204, 526, 62, 269, 100, 112, 358, 661, 78, 157, 38, 633, 768, 254, 689, 200, 250, 144, 503, 0, 237, 246, 484, 120, 417, 213, 31, 651, 408],\n",
    " [139, 582, 279, 647, 54, 68, 730, 646, 561, 581, 750, 71, 451, 551, 694, 628, 247, 281, 71, 52, 72, 706, 84, 718, 106, 182, 248, 764, 581, 698, 476, 10, 757, 570, 397, 660, 137, 47, 473, 546, 285, 745, 69, 767, 731, 631, 234, 260, 772, 53],\n",
    " [404, 27, 210, 559, 12, 439, 112, 117, 411, 568, 425, 171, 115, 382, 60, 310, 695, 447, 239, 763, 241, 205, 545, 469, 679, 67, 579, 89, 167, 478, 71, 646, 739, 332, 632, 355, 165, 160, 294, 111, 720, 251, 397, 162, 553, 591, 640, 26, 762, 20],\n",
    " [601, 374, 576, 420, 496, 134, 188, 335, 736, 192, 287, 322, 312, 566, 409, 327, 298, 329, 16, 519, 148, 433, 91, 464, 312, 165, 718, 388, 299, 548, 589, 302, 354, 70, 650, 605, 379, 405, 463, 637, 771, 587, 708, 155, 325, 678, 751, 667, 248, 371],\n",
    " [78, 732, 62, 650, 691, 171, 462, 633, 605, 268, 546, 606, 588, 70, 562, 675, 90, 779, 633, 370, 277, 749, 629, 648, 431, 79, 32, 357, 417, 408, 724, 28, 22, 272, 25, 746, 656, 574, 530, 591, 137, 531, 288, 40, 493, 401, 337, 261, 731, 399],\n",
    " [428, 684, 233, 427, 699, 771, 236, 154, 216, 566, 474, 746, 467, 48, 16, 573, 166, 576, 555, 611, 660, 715, 689, 308, 769, 301, 178, 90, 107, 226, 503, 770, 439, 731, 426, 505, 655, 480, 771, 117, 62, 414, 319, 118, 437, 670, 50, 316, 520, 41],\n",
    " [303, 513, 26, 573, 323, 720, 410, 457, 196, 300, 689, 10, 276, 432, 57, 48, 296, 65, 181, 760, 619, 45, 566, 495, 489, 682, 362, 250, 681, 221, 121, 210, 24, 56, 769, 237, 498, 365, 713, 496, 37, 337, 750, 82, 748, 748, 741, 37, 769, 55],\n",
    " [486, 706, 451, 102, 459, 538, 526, 227, 461, 266, 319, 128, 731, 380, 765, 154, 745, 527, 530, 629, 270, 409, 545, 332, 242, 557, 40, 719, 7, 551, 665, 726, 702, 231, 46, 746, 279, 326, 761, 499, 27, 276, 586, 777, 559, 656, 369, 551, 611, 622],\n",
    " [158, 34, 394, 283, 0, 719, 685, 30, 513, 28, 254, 464, 689, 165, 743, 236, 17, 164, 479, 42, 357, 123, 282, 677, 525, 462, 11, 664, 682, 327, 401, 83, 744, 211, 203, 92, 197, 157, 465, 511, 276, 547, 652, 116, 610, 190, 261, 319, 67, 76],\n",
    " [54, 714, 460, 463, 221, 481, 317, 529, 277, 551, 215, 60, 25, 446, 233, 780, 296, 89, 185, 55, 208, 686, 191, 693, 733, 616, 176, 171, 760, 392, 600, 269, 102, 326, 71, 135, 553, 135, 217, 53, 27, 569, 405, 70, 394, 687, 613, 243, 427, 194],\n",
    " [334, 262, 121, 449, 542, 717, 74, 756, 652, 31, 69, 415, 38, 189, 393, 61, 144, 749, 406, 307, 706, 486, 339, 513, 249, 183, 163, 59, 244, 96, 657, 572, 420, 378, 747, 389, 522, 480, 350, 666, 516, 102, 621, 519, 179, 254, 502, 505, 568, 128],\n",
    " [613, 623, 79, 551, 543, 209, 335, 149, 47, 636, 694, 481, 692, 110, 450, 621, 491, 376, 265, 714, 288, 561, 511, 482, 752, 132, 196, 155, 52, 489, 602, 155, 549, 650, 87, 777, 745, 483, 741, 726, 66, 509, 659, 334, 64, 654, 470, 111, 454, 291],\n",
    " [27, 279, 556, 533, 771, 323, 16, 206, 711, 660, 185, 630, 467, 464, 774, 171, 341, 599, 243, 610, 265, 686, 707, 715, 528, 337, 539, 759, 47, 624, 521, 246, 13, 577, 2, 504, 76, 115, 536, 373, 596, 635, 620, 512, 206, 419, 561, 82, 399, 16],\n",
    " [188, 60, 778, 264, 320, 703, 230, 449, 470, 131, 728, 561, 359, 445, 546, 177, 281, 727, 47, 612, 589, 279, 212, 438, 33, 63, 82, 76, 327, 128, 60, 350, 165, 189, 657, 663, 344, 334, 85, 579, 177, 453, 352, 768, 261, 541, 687, 634, 440, 172],\n",
    " [44, 433, 602, 337, 422, 509, 310, 14, 78, 262, 449, 639, 474, 692, 178, 331, 372, 134, 467, 42, 592, 536, 637, 159, 179, 67, 414, 373, 73, 59, 327, 107, 396, 137, 779, 122, 395, 208, 286, 327, 584, 118, 130, 732, 73, 731, 487, 682, 530, 675],\n",
    " [728, 241, 286, 656, 484, 160, 734, 218, 627, 549, 531, 505, 471, 173, 223, 161, 125, 37, 411, 487, 472, 570, 140, 748, 440, 446, 53, 721, 715, 490, 117, 610, 33, 180, 277, 282, 168, 548, 602, 244, 733, 750, 587, 723, 298, 297, 35, 111, 185, 496],\n",
    " [526, 82, 353, 492, 430, 463, 411, 151, 722, 331, 223, 655, 298, 124, 482, 719, 471, 265, 563, 231, 726, 560, 134, 780, 141, 85, 561, 340, 405, 389, 154, 166, 1, 645, 647, 747, 336, 628, 316, 283, 634, 623, 476, 368, 634, 158, 773, 395, 442, 437],\n",
    " [86, 220, 269, 669, 308, 309, 261, 57, 202, 767, 358, 393, 698, 337, 188, 387, 147, 324, 9, 429, 754, 612, 277, 726, 758, 703, 454, 224, 106, 81, 185, 95, 345, 157, 104, 38, 307, 37, 523, 329, 740, 331, 489, 349, 719, 515, 149, 100, 627, 432],\n",
    " [253, 447, 287, 206, 205, 459, 234, 107, 315, 637, 163, 703, 125, 450, 618, 366, 72, 211, 172, 763, 766, 316, 508, 279, 563, 38, 200, 143, 632, 111, 693, 297, 469, 757, 592, 124, 656, 174, 666, 592, 188, 417, 594, 363, 573, 655, 399, 767, 527, 662],\n",
    " [128, 441, 495, 540, 553, 209, 506, 410, 40, 68, 178, 304, 262, 38, 581, 62, 78, 492, 232, 314, 245, 123, 381, 504, 426, 94, 161, 455, 697, 156, 316, 573, 689, 331, 344, 114, 676, 488, 634, 367, 691, 736, 353, 244, 162, 245, 619, 567, 414, 563],\n",
    " [205, 632, 377, 108, 97, 526, 634, 196, 761, 761, 757, 136, 279, 399, 331, 769, 484, 122, 383, 281, 93, 598, 426, 268, 30, 192, 301, 734, 613, 494, 296, 75, 208, 85, 584, 57, 580, 579, 108, 398, 465, 408, 172, 136, 269, 281, 659, 6, 398, 162],\n",
    " [711, 442, 704, 123, 554, 50, 271, 246, 160, 4, 271, 3, 759, 621, 610, 33, 777, 29, 49, 30, 164, 347, 124, 1, 208, 723, 280, 50, 588, 687, 191, 606, 124, 91, 512, 358, 234, 140, 761, 496, 685, 67, 74, 586, 769, 123, 201, 337, 192, 403],\n",
    " [643, 163, 439, 138, 170, 9, 453, 255, 693, 302, 389, 465, 175, 153, 465, 152, 759, 388, 70, 378, 573, 138, 560, 114, 305, 543, 619, 640, 432, 429, 614, 115, 416, 267, 78, 520, 176, 568, 406, 363, 727, 81, 308, 665, 405, 326, 549, 409, 402, 0],\n",
    " [196, 25, 751, 666, 169, 460, 523, 217, 11, 703, 321, 707, 352, 4, 460, 390, 347, 657, 60, 139, 352, 667, 355, 203, 627, 153, 404, 539, 25, 255, 714, 502, 641, 759, 52, 764, 116, 773, 286, 285, 64, 741, 102, 335, 195, 195, 762, 753, 274, 1],\n",
    " [459, 146, 415, 190, 103, 661, 560, 736, 335, 313, 49, 713, 178, 307, 214, 305, 767, 473, 225, 236, 443, 695, 491, 335, 238, 692, 287, 316, 721, 425, 738, 746, 318, 193, 54, 153, 182, 78, 346, 653, 325, 38, 1, 490, 128, 348, 104, 275, 415, 111],\n",
    " [231, 331, 174, 660, 376, 114, 672, 278, 751, 337, 208, 191, 103, 739, 532, 18, 725, 732, 497, 636, 420, 515, 467, 673, 204, 406, 693, 778, 385, 603, 730, 281, 236, 645, 707, 521, 342, 82, 7, 444, 650, 375, 259, 140, 367, 461, 239, 130, 148, 576],\n",
    " [590, 707, 16, 648, 144, 113, 613, 421, 122, 84, 762, 258, 552, 306, 458, 478, 105, 700, 389, 539, 471, 749, 180, 147, 590, 421, 727, 771, 358, 37, 298, 671, 592, 420, 740, 717, 708, 174, 293, 159, 610, 143, 710, 325, 208, 674, 4, 208, 697, 493],\n",
    " [207, 128, 768, 191, 598, 649, 433, 740, 148, 277, 316, 231, 18, 699, 520, 779, 677, 620, 350, 658, 278, 733, 38, 478, 760, 432, 541, 178, 361, 618, 10, 546, 417, 589, 329, 121, 605, 222, 409, 597, 183, 633, 120, 463, 727, 447, 324, 515, 514, 123],\n",
    " [502, 162, 706, 292, 322, 620, 600, 422, 12, 111, 366, 176, 10, 508, 609, 760, 561, 197, 92, 233, 412, 675, 190, 562, 401, 434, 391, 282, 580, 346, 173, 119, 438, 296, 501, 157, 266, 724, 257, 492, 358, 31, 662, 85, 30, 694, 131, 643, 50, 571],\n",
    " [357, 658, 693, 98, 83, 1, 12, 568, 201, 436, 384, 272, 735, 404, 12, 27, 18, 683, 413, 548, 332, 117, 705, 636, 540, 215, 420, 448, 767, 107, 623, 358, 235, 392, 378, 508, 90, 436, 613, 28, 524, 550, 12, 54, 119, 562, 133, 147, 453, 207],\n",
    " [755, 457, 419, 447, 638, 671, 253, 720, 533, 716, 730, 396, 539, 374, 65, 321, 419, 121, 62, 236, 550, 495, 306, 518, 275, 554, 83, 749, 218, 170, 81, 105, 639, 373, 315, 760, 729, 407, 497, 723, 38, 699, 294, 423, 429, 646, 694, 293, 272, 352],\n",
    " [403, 260, 500, 771, 512, 178, 535, 594, 658, 569, 720, 363, 390, 515, 227, 445, 686, 49, 693, 302, 259, 516, 94, 390, 770, 552, 288, 270, 143, 448, 467, 429, 567, 85, 422, 314, 779, 481, 292, 732, 384, 669, 624, 379, 117, 170, 42, 348, 222, 200],\n",
    " [429, 344, 323, 464, 712, 588, 279, 636, 500, 182, 207, 376, 370, 87, 164, 570, 370, 630, 779, 670, 394, 308, 353, 470, 439, 680, 149, 645, 567, 318, 780, 100, 732, 268, 542, 745, 237, 151, 684, 455, 439, 775, 369, 127, 439, 537, 766, 98, 434, 420],\n",
    " [617, 199, 509, 504, 629, 2, 103, 206, 517, 45, 384, 151, 568, 80, 298, 774, 417, 42, 288, 627, 614, 746, 45, 183, 216, 215, 609, 457, 546, 736, 248, 700, 119, 328, 373, 113, 329, 649, 702, 169, 680, 94, 37, 343, 739, 128, 107, 686, 272, 646],\n",
    " [2, 596, 108, 305, 26, 735, 584, 572, 224, 714, 144, 38, 709, 150, 701, 766, 232, 150, 370, 315, 32, 570, 279, 691, 405, 652, 19, 395, 98, 51, 177, 383, 765, 92, 174, 561, 430, 16, 128, 497, 68, 360, 681, 571, 554, 147, 426, 622, 395, 384],\n",
    " [676, 251, 610, 716, 98, 332, 636, 196, 409, 717, 23, 721, 625, 594, 482, 596, 576, 658, 632, 391, 52, 570, 98, 186, 244, 596, 242, 506, 688, 283, 762, 376, 669, 143, 244, 76, 691, 106, 336, 339, 152, 717, 480, 339, 144, 138, 134, 671, 672, 707],\n",
    " [168, 232, 571, 512, 757, 213, 696, 153, 227, 66, 603, 401, 383, 501, 682, 274, 165, 320, 141, 97, 393, 485, 100, 368, 463, 368, 435, 708, 547, 101, 535, 682, 557, 536, 164, 288, 474, 534, 357, 317, 551, 43, 62, 225, 484, 354, 492, 97, 277, 222],\n",
    " [516, 322, 456, 373, 50, 291, 692, 229, 495, 619, 738, 58, 606, 38, 313, 89, 500, 152, 391, 492, 56, 639, 719, 460, 652, 36, 657, 400, 409, 78, 675, 659, 531, 56, 150, 516, 590, 438, 225, 714, 713, 394, 92, 196, 438, 483, 571, 321, 94, 466],\n",
    " [727, 8, 742, 31, 105, 514, 345, 747, 650, 667, 77, 576, 240, 185, 348, 647, 328, 37, 83, 256, 190, 130, 73, 408, 207, 603, 321, 642, 77, 49, 434, 270, 453, 304, 449, 204, 105, 655, 403, 565, 414, 463, 761, 426, 218, 106, 76, 338, 654, 428],\n",
    " [189, 440, 621, 780, 156, 41, 47, 748, 484, 204, 654, 73, 689, 604, 92, 358, 526, 636, 618, 45, 731, 67, 585, 466, 506, 351, 549, 269, 357, 749, 195, 520, 275, 331, 195, 565, 62, 123, 507, 741, 720, 105, 591, 142, 167, 477, 175, 558, 23, 158],\n",
    " [385, 137, 322, 339, 692, 44, 17, 109, 316, 634, 162, 304, 554, 599, 434, 124, 526, 657, 60, 539, 28, 769, 213, 590, 499, 174, 463, 244, 20, 65, 384, 465, 315, 22, 469, 478, 535, 30, 89, 19, 731, 353, 402, 241, 271, 172, 113, 684, 7, 532],\n",
    " [693, 538, 232, 485, 362, 649, 605, 138, 73, 587, 536, 351, 426, 64, 221, 208, 32, 413, 246, 440, 495, 210, 766, 64, 159, 331, 77, 688, 510, 726, 193, 87, 518, 572, 399, 257, 497, 591, 484, 449, 268, 73, 402, 63, 211, 341, 545, 130, 196, 614],\n",
    " [379, 120, 40, 113, 83, 83, 406, 63, 769, 31, 204, 413, 767, 532, 717, 651, 258, 433, 266, 149, 649, 482, 219, 401, 287, 696, 583, 43, 125, 300, 315, 772, 77, 289, 574, 476, 410, 760, 93, 768, 420, 112, 690, 221, 119, 763, 533, 163, 515, 422],\n",
    " [437, 664, 645, 536, 517, 222, 426, 99, 348, 427, 319, 356, 339, 559, 528, 16, 569, 749, 686, 748, 324, 152, 632, 576, 87, 276, 693, 517, 623, 675, 592, 65, 616, 558, 486, 419, 252, 228, 240, 234, 588, 478, 620, 386, 7, 227, 14, 281, 625, 278],\n",
    " [241, 544, 294, 415, 188, 620, 734, 252, 224, 688, 731, 93, 2, 267, 467, 693, 343, 725, 302, 387, 476, 617, 526, 596, 219, 233, 602, 171, 233, 474, 675, 113, 694, 361, 647, 119, 290, 449, 377, 742, 586, 20, 521, 664, 237, 625, 7, 660, 562, 259],\n",
    " [7, 656, 407, 455, 759, 513, 677, 503, 127, 348, 498, 209, 708, 182, 242, 662, 519, 263, 123, 529, 125, 172, 586, 213, 140, 294, 207, 138, 218, 552, 704, 75, 263, 331, 195, 335, 479, 102, 278, 162, 760, 205, 338, 417, 303, 687, 685, 78, 732, 645],\n",
    " [110, 722, 26, 317, 642, 364, 359, 345, 115, 33, 39, 280, 472, 699, 375, 336, 345, 733, 767, 465, 646, 172, 253, 771, 11, 518, 291, 318, 25, 173, 15, 449, 56, 422, 461, 378, 398, 726, 591, 615, 603, 100, 480, 628, 452, 594, 504, 653, 611, 682],\n",
    " [135, 551, 547, 467, 675, 574, 85, 42, 137, 748, 730, 712, 85, 660, 175, 633, 559, 476, 390, 264, 623, 4, 542, 327, 491, 445, 285, 4, 199, 17, 236, 705, 462, 352, 508, 234, 752, 478, 347, 562, 191, 553, 104, 276, 778, 654, 0, 759, 51, 519],\n",
    " [352, 180, 360, 251, 454, 144, 53, 447, 103, 191, 135, 488, 444, 59, 417, 489, 422, 489, 586, 384, 93, 299, 737, 535, 302, 3, 444, 448, 204, 195, 640, 156, 587, 440, 614, 766, 516, 117, 157, 581, 705, 580, 60, 15, 767, 358, 232, 283, 230, 340],\n",
    " [54, 17, 732, 206, 9, 643, 250, 163, 738, 109, 36, 519, 708, 555, 158, 316, 194, 242, 499, 484, 626, 300, 101, 406, 719, 541, 30, 702, 180, 578, 444, 441, 457, 547, 530, 483, 624, 235, 119, 79, 744, 290, 262, 668, 95, 629, 442, 475, 733, 544]]\n",
    "\"\"\")\n",
    "\n",
    "TEST_DATA_2 = json.loads(\"\"\"\n",
    "[[6,5,0,8,1,8],\n",
    "[3,6,1,3,6,3],\n",
    "[9,5,7,9,1,1],\n",
    "[8,7,9,4,8,3],\n",
    "[7,8,7,6,2,5],\n",
    "[3,4,0,5,0,4]]\n",
    "\"\"\")\n",
    "\n",
    "TEST_DATA_3 = json.loads(\"\"\"\n",
    "[\n",
    "[0,1,2],\n",
    "[0,1,2],\n",
    "[0,1,2]\n",
    "]\n",
    "\"\"\")\n",
    "\n",
    "def solve(graph):\n",
    "    graph = nx.DiGraph()\n",
    "\n",
    "    num_rows = len(data)\n",
    "    num_cols = len(data[0])\n",
    "    max_value = max([max(row) for row in data])\n",
    "    for row in range(num_rows):\n",
    "        for col in range(num_cols):\n",
    "            this_node = (row,col)\n",
    "            this_value = data[row][col]\n",
    "            if col < num_cols - 1:\n",
    "                right_node = (row,col+1)\n",
    "                right_value = data[row][col+1]\n",
    "                graph.add_edge(this_node, right_node, \n",
    "                               distance=right_value, \n",
    "                               inv_distance=max_value-right_value)\n",
    "            if row < num_rows - 1:\n",
    "                bottom_node = (row+1,col)\n",
    "                bottom_value = data[row+1][col]\n",
    "                graph.add_edge(this_node, bottom_node, \n",
    "                               distance=bottom_value, \n",
    "                               inv_distance=max_value-bottom_value)\n",
    "    path = nx.shortest_path(\n",
    "        graph, source=(0,0), target=(num_rows-1,num_cols-1), weight=\"inv_distance\")\n",
    "    score = sum(map(lambda pos: data[pos[0]][pos[1]], path))\n",
    "    return (score, graph, path)\n",
    "    \n",
    "def visualize(data, graph, path, isLarge):\n",
    "    path_edges = list(zip(path, path[1:]))\n",
    "    path_edge_labels = {}\n",
    "    s = data[0][0]\n",
    "    for from_node, to_node in path_edges:\n",
    "        path_edge_labels[(from_node, to_node)] = str(s)\n",
    "        s += data[to_node[0]][to_node[1]]\n",
    "        \n",
    "    node_labels = {node: \"%d\\n(%d,%d)\" % (\n",
    "        data[node[0]][node[1]], \n",
    "        node[0], \n",
    "        node[1]) for node in graph.nodes()}\n",
    "    \n",
    "    positions = {node: [node[1], node[0]] for node in graph.nodes()}\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    fig, ax = plt.subplots(figsize=(8,8))    \n",
    "    if isLarge:\n",
    "        nx.draw_networkx_nodes(graph, positions, ax=ax, node_color=\"#eeeeee\", node_size=30)\n",
    "        nx.draw_networkx_nodes(graph, positions, ax=ax, nodelist=path, node_color=\"#ff3800\", node_size=30)\n",
    "        nx.draw_networkx_edges(graph, positions, ax=ax, edgelist=path_edges, edge_color=\"#ff3800\", width=1)        \n",
    "    else:\n",
    "        nx.draw_networkx_nodes(graph, positions, ax=ax, node_color=\"#eeeeee\")\n",
    "        nx.draw_networkx_labels(graph, positions, ax=ax, font_weight=\"bold\", labels=node_labels)\n",
    "        nx.draw_networkx_edges(graph, positions, ax=ax, edge_color=\"#eeeeee\")\n",
    "        nx.draw_networkx_nodes(graph, positions, ax=ax, nodelist=path, node_color=\"#ff3800\")\n",
    "        nx.draw_networkx_edges(graph, positions, ax=ax, edgelist=path_edges, edge_color=\"#ff3800\", width=3)\n",
    "        nx.draw_networkx_edge_labels(graph, positions, ax=ax, edge_labels=path_edge_labels)\n",
    "        \n",
    "    ax.xaxis.tick_top()\n",
    "    ax.invert_yaxis()\n",
    "    plt.show()\n",
    "    \n",
    "def make_commands(path):\n",
    "    commands = \"\"\n",
    "    for i in range(1, len(path)):\n",
    "        previous_pos = path[i - 1]\n",
    "        this_pos = path[i]\n",
    "        if previous_pos[0] < this_pos[0]:\n",
    "            commands += \"D\"\n",
    "        elif previous_pos[1] < this_pos[1]:\n",
    "            commands += \"R\"\n",
    "    return commands\n",
    "\n",
    "experiments = [\n",
    "    (TEST_DATA_3, False),\n",
    "    (TEST_DATA_2, False),\n",
    "    (TEST_DATA_1, True)\n",
    "]\n",
    "for i, experiment in enumerate(experiments):\n",
    "    data, isLarge = experiment\n",
    "    (score, graph, path) = solve(data)\n",
    "    commands = make_commands(path)\n",
    "    print(\"Experiment #%d\" % (i + 1))\n",
    "    print(\"score: %d\\ncommands: %s\" % (score, commands))\n",
    "    visualize(data, graph, path, isLarge)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}