Computes the Multiscale Graph Correlation (MGC) test statistic.
\n Specifically, for each point, MGC finds the \\(k\\)-nearest neighbors for\n one property (e.g. cloud density), and the \\(l\\)-nearest neighbors for\n the other property (e.g. grass wetness) [1]. This pair \\((k, l)\\) is\n called the \u201cscale\u201d. A priori, however, it is not know which scales will be\n most informative. So, MGC computes all distance pairs, and then efficiently\n computes the distance correlations for all scales. The local correlations\n"}, {"source1": "./usr/share/doc/python-scipy-doc/html/searchindex.js", "source2": "./usr/share/doc/python-scipy-doc/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -4343,18 +4343,18 @@\n \"0rc1\": [32, 3376, 3383, 3386],\n \"0rc2\": [3372, 3376],\n \"0th\": [385, 386, 510, 515, 1785, 1786],\n \"0x00\": 562,\n \"0x7fe753763180\": 545,\n \"0x_3\": 3427,\n \"0x_4\": 3427,\n- \"0xb1a484f0\": 3127,\n- \"0xb280c928\": 1545,\n- \"0xb28d7778\": 61,\n- \"0xb6310ad8\": 3045,\n+ \"0xf1935c40\": 61,\n+ \"0xf1c338e0\": 3127,\n+ \"0xf28e3028\": 1545,\n+ \"0xf636ead8\": 3045,\n \"100\": [10, 16, 41, 101, 143, 280, 287, 288, 356, 464, 497, 498, 510, 512, 513, 515, 563, 716, 1405, 1446, 1537, 1538, 1541, 1542, 1557, 1560, 1561, 1563, 1565, 1566, 1572, 1575, 1578, 1579, 1582, 1586, 1588, 1624, 1626, 1627, 1628, 1631, 1632, 1638, 1639, 1640, 1641, 1647, 1651, 1653, 1654, 1659, 1667, 1676, 1691, 1694, 1697, 1699, 1701, 1702, 1705, 1727, 1739, 1741, 1743, 1748, 1750, 1751, 1754, 1755, 1756, 1759, 1767, 1785, 1796, 1812, 2366, 2439, 2446, 2475, 2508, 2533, 2561, 2658, 2673, 2678, 2679, 2701, 2704, 2749, 2754, 2815, 2834, 2862, 2900, 2903, 2905, 2906, 2908, 2910, 2912, 2919, 2923, 2925, 2926, 2927, 2928, 2929, 2938, 2940, 2943, 2946, 2951, 2952, 2953, 2954, 2955, 2957, 2960, 2962, 2963, 2965, 2966, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2992, 2994, 2996, 2997, 2998, 2999, 3000, 3001, 3004, 3005, 3006, 3007, 3008, 3009, 3012, 3013, 3014, 3015, 3018, 3020, 3023, 3024, 3025, 3026, 3028, 3029, 3031, 3032, 3033, 3035, 3036, 3037, 3038, 3040, 3041, 3044, 3045, 3046, 3048, 3052, 3127, 3132, 3134, 3135, 3136, 3138, 3140, 3143, 3144, 3146, 3151, 3152, 3153, 3156, 3161, 3162, 3163, 3165, 3174, 3192, 3198, 3215, 3223, 3242, 3244, 3245, 3247, 3250, 3251, 3252, 3254, 3260, 3261, 3265, 3266, 3270, 3273, 3275, 3278, 3279, 3280, 3282, 3283, 3286, 3287, 3316, 3323, 3327, 3328, 3345, 3348, 3383, 3385, 3390, 3394, 3396, 3406, 3417, 3419, 3422, 3423, 3427, 3428, 3429, 3430, 3431, 3480, 3516],\n \"1000\": [100, 287, 371, 401, 485, 501, 742, 1049, 1537, 1539, 1549, 1550, 1555, 1556, 1631, 1638, 1639, 1651, 1662, 1676, 1679, 1688, 1689, 1691, 1692, 1694, 1699, 1701, 1703, 1711, 1715, 1745, 1746, 1777, 1802, 1815, 2362, 2365, 2621, 2658, 2659, 2679, 2701, 2747, 2749, 2750, 2759, 2900, 2903, 2905, 2906, 2909, 2910, 2911, 2912, 2916, 2918, 2920, 2923, 2925, 2926, 2927, 2928, 2929, 2938, 2940, 2941, 2943, 2945, 2946, 2951, 2952, 2953, 2954, 2955, 2957, 2960, 2962, 2963, 2965, 2966, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3004, 3005, 3006, 3007, 3012, 3013, 3014, 3015, 3020, 3024, 3025, 3026, 3027, 3028, 3029, 3031, 3032, 3033, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3044, 3048, 3052, 3127, 3132, 3133, 3134, 3135, 3136, 3138, 3139, 3140, 3143, 3144, 3147, 3149, 3151, 3152, 3153, 3157, 3161, 3162, 3163, 3164, 3165, 3171, 3196, 3220, 3241, 3244, 3248, 3250, 3254, 3260, 3261, 3265, 3266, 3273, 3275, 3278, 3279, 3280, 3282, 3283, 3287, 3289, 3292, 3293, 3306, 3316, 3321, 3326, 3329, 3342, 3361, 3372, 3396, 3400, 3406, 3417, 3423, 3427, 3430, 3431],\n \"10000\": [280, 282, 1572, 1663, 1669, 1687, 1729, 1802, 2645, 2913, 3019, 3154, 3155, 3431],\n \"100000\": [10, 1565, 1715, 1743, 2908, 3011, 3215],\n \"1000000\": [1343, 2532, 3045, 3129],\n \"10000000\": [20, 1550, 1557],\n \"100000000\": [282, 2367, 2368],\n"}]}]}]}]}]}