孤独症项目（3）

今天一刷朋友圈，一个小学同学订婚了。老实讲确实是羡慕了，到我这个年龄，游戏的吸引力已经下降了很多了，我需要性和爱。可是我身高，帅气，money什么都没有，所以我怎么办呢。我把那个同学屏蔽了，哈哈，事情就是这样，我抢不过别的男生，因为我个太矮，家太穷，自己又没有能力，所以我只能视而不见，活在自己的小世界里就好啦。开始今天的学习吧。 1 这个npy文件我知道咋回事了

{'timeseires': array([[[ -4.671351, -12.931799, -20.238678, ..., -4.6999 , -2.881232, -5.373659], [ -6.610331, -10.146871, -11.706947, ..., -8.545325, -4.824956, -3.549801], [ 6.539966, -3.924838, -22.075667, ..., -10.810907, -9.507657, -11.782674], ..., [ 23.815056, 14.159346, -4.235137, ..., 2.318333, -0.419391, -6.08656 ], [ 29.358294, 29.569822, 12.97839 , ..., -35.478521, -32.452544, -38.755226], [ 12.700474, 10.523634, -1.103658, ..., 16.980012, 18.994251, 7.554179]], [[ 15.055096, 14.30167 , 8.35954 , ..., -2.320052, 5.738047, 18.929523], [ -2.650435, -5.070064, -6.582098, ..., -6.500024, -6.520322, -2.209336], [ 7.958244, 7.113133, 3.146248, ..., -7.816352, 0.160806, 12.985252], ..., [ 0.216612, -7.946295, -14.338741, ..., -2.661149, -1.293657, 4.052661], [ 0.595963, -1.462514, -3.352045, ..., -7.601621, -1.38041 , 7.066522], [ -1.754612, -2.342497, -2.100473, ..., -4.888682, 0.817308, 6.089231]], [[ 10.699614, 12.640328, 9.107096, ..., 25.303575, 14.631509, -2.431361], [ 8.946028, 11.992252, 10.768465, ..., 17.12695 , 4.901006, -9.559137], [ 12.502747, 17.729441, 17.117685, ..., 13.455708, -13.292004, -35.746319], ..., [-13.968082, -18.558227, -20.739389, ..., 30.461445, 48.840466, 47.846905], [ 7.775516, 7.945796, 4.77154 , ..., 35.356228, 42.096015, 35.434606], [ 3.752828, 5.027397, 4.62332 , ..., 22.339197, 34.736423, 33.95411 ]], ..., [[ -9.594532, -18.276965, -12.977462, ..., 22.482481, 9.076281, -3.665357], [ -1.753394, -3.048763, -0.66247 , ..., 3.479453, 6.685637, 5.947072], [ -7.053816, -4.420122, 6.336983, ..., 23.162804, 14.313564, 2.746504], ..., [ 14.337799, 18.14827 , -0.895313, ..., 37.246694, 58.276117, 34.074897], [-15.100426, -22.201254, -24.41196 , ..., 60.251682, 66.325714, 32.992202], [-13.52109 , -18.166892, -16.496382, ..., 38.908534, 41.846779, 15.183713]], [[ -4.960212, -10.467057, -2.350238, ..., 8.175621, -3.594708, -13.050947], [ -2.188337, -5.102677, -1.05976 , ..., 3.500581, 5.308049, -0.665579], [ -7.689325, -4.299846, 4.479113, ..., 7.190843, -6.158385, -10.379174], ..., [ 4.838299, -1.461336, 3.449214, ..., 22.214853, 10.277305, -20.902231], [ -9.16024 , -6.62222 , 5.643113, ..., 1.50843 , -7.206676, -21.635724], [ -7.149388, -5.05326 , 5.05269 , ..., 12.605102, 7.582936, -10.857408]], [[ -8.999973, -8.265457, 0.224464, ..., 2.243862, -2.492217, -13.048096], [ -1.115053, 0.311383, -0.91307 , ..., 3.98221 , 1.695672, -5.227182], [ 2.39671 , 2.200741, 5.486765, ..., 13.607921, 7.737062, -13.571547], ..., [-24.200021, -15.751269, 3.40317 , ..., 3.005727, 3.171528, -1.583004], [-12.074557, -7.229352, -1.982399, ..., 4.310723, -9.359018, -3.962187], [ -7.750716, -6.153427, -2.874191, ..., 4.213236, -2.779235, -1.473658]]], shape=(1009, 111, 100)), 'label': array([0., 0., 0., ..., 0., 0., 0.], shape=(1009,)), 'corr': array([[[ 0. , 0.97663709, 0.82530072, ..., 0.13740499, 0.35407144, 0.08548723], [ 0.97663709, 0. , 0.81736156, ..., 0.12545135, 0.31653123, 0.08523937], [ 0.82530072, 0.81736156, 0. , ..., 0.32199278, 0.48073326, 0.23490678], ..., [ 0.13740499, 0.12545135, 0.32199278, ..., 0. , 0.91433765, 0.76759212], [ 0.35407144, 0.31653123, 0.48073326, ..., 0.91433765, 0. , 1.1123392 ], [ 0.08548723, 0.08523937, 0.23490678, ..., 0.76759212, 1.1123392 , 0. ]], [[ 0. , 0.62647921, 0.68386934, ..., -0.05088815, 0.60446506, 0.41812769], [ 0.62647921, 0. , 0.54538175, ..., 0.09402108, 0.39961469, 0.28481799], [ 0.68386934, 0.54538175, 0. , ..., -0.06276508, 0.30605398, 0.22979597], ..., [-0.05088815, 0.09402108, -0.06276508, ..., 0. , -0.01265819, 0.08046417], [ 0.60446506, 0.39961469, 0.30605398, ..., -0.01265819, 0. , 0.85968089], [ 0.41812769, 0.28481799, 0.22979597, ..., 0.08046417, 0.85968089, 0. ]], [[ 0. , 0.52387629, 0.39586813, ..., 0.49770721, 0.77686881, 0.67761376], [ 0.52387629, 0. , 0.43492339, ..., 0.08623291, 0.30371602, 0.28660143], [ 0.39586813, 0.43492339, 0. , ..., -0.10309543, -0.00903498, -0.06115111], ..., [ 0.49770721, 0.08623291, -0.10309543, ..., 0. , 0.69001642, 0.72559831], [ 0.77686881, 0.30371602, -0.00903498, ..., 0.69001642, 0. , 1.37230362], [ 0.67761376, 0.28660143, -0.06115111, ..., 0.72559831, 1.37230362, 0. ]], ..., [[ 0. , 0.28196335, 0.62367273, ..., 0.31654042, 0.30184585, 0.24758647], [ 0.28196335, 0. , 0.52368336, ..., -0.05335835, -0.01349599, -0.08051841], [ 0.62367273, 0.52368336, 0. , ..., 0.07688247, 0.15630406, 0.09915298], ..., [ 0.31654042, -0.05335835, 0.07688247, ..., 0. , 0.33613925, 0.35266004], [ 0.30184585, -0.01349599, 0.15630406, ..., 0.33613925, 0. , 1.26734374], [ 0.24758647, -0.08051841, 0.09915298, ..., 0.35266004, 1.26734374, 0. ]], [[ 0. , 0.3318541 , 0.3479959 , ..., 0.17348234, 0.33035729, 0.28096653], [ 0.3318541 , 0. , 0.13110274, ..., 0.16131239, 0.18152441, 0.0901034 ], [ 0.3479959 , 0.13110274, 0. , ..., 0.03690945, 0.19735146, 0.23887414], ..., [ 0.17348234, 0.16131239, 0.03690945, ..., 0. , 0.21702285, 0.29280098], [ 0.33035729, 0.18152441, 0.19735146, ..., 0.21702285, 0. , 0.63961068], [ 0.28096653, 0.0901034 , 0.23887414, ..., 0.29280098, 0.63961068, 0. ]], [[ 0. , 0.61490946, 0.86683146, ..., 0.28751356, 0.35820952, 0.36030001], [ 0.61490946, 0. , 0.44664105, ..., 0.39767059, 0.28708429, 0.39182893], [ 0.86683146, 0.44664105, 0. , ..., 0.18048986, 0.38597263, 0.43650839], ..., [ 0.28751356, 0.39767059, 0.18048986, ..., 0. , 0.22311195, 0.35032753], [ 0.35820952, 0.28708429, 0.38597263, ..., 0.22311195, 0. , 1.03944121], [ 0.36030001, 0.39182893, 0.43650839, ..., 0.35032753, 1.03944121, 0. ]]], shape=(1009, 111, 111)), 'pcorr': array([[[ 0. , 0.0845738 , 0.05283968, ..., -0.02852326, -0.05030299, -0.00900934], [ 0.0845738 , 0. , 0.05794923, ..., -0.06134557, 0.02918709, -0.04988144], [ 0.05283968, 0.05794923, 0. , ..., 0.08585561, 0.0933945 , -0.01767182], ..., [-0.02852326, -0.06134557, 0.08585561, ..., 0. , 0.0742375 , 0.11123407], [-0.05030299, 0.02918709, 0.0933945 , ..., 0.0742375 , 0. , 0.26601118], [-0.00900934, -0.04988144, -0.01767182, ..., 0.11123407, 0.26601118, 0. ]], [[ 0. , 0.02515851, -0.02687615, ..., -0.07865261, 0.03765896, 0.03533854], [ 0.02515851, 0. , 0.03731194, ..., -0.04081472, -0.02018316, 0.02902786], [-0.02687615, 0.03731194, 0. , ..., 0.02341885, -0.0237432 , 0.07630574], ..., [-0.07865261, -0.04081472, 0.02341885, ..., 0. , 0.05092006, 0.03885538], [ 0.03765896, -0.02018316, -0.0237432 , ..., 0.05092006, 0. , 0.08766317], [ 0.03533854, 0.02902786, 0.07630574, ..., 0.03885538, 0.08766317, 0. ]], [[ 0. , 0.0345411 , 0.02792029, ..., -0.01667542, 0.01530761, -0.00780056], [ 0.0345411 , 0. , 0.05455556, ..., 0.0643729 , 0.01796368, 0.03800115], [ 0.02792029, 0.05455556, 0. , ..., -0.01042823, -0.03121966, 0.01426741], ..., [-0.01667542, 0.0643729 , -0.01042823, ..., 0. , -0.07791113, -0.04875181], [ 0.01530761, 0.01796368, -0.03121966, ..., -0.07791113, 0. , 0.13051308], [-0.00780056, 0.03800115, 0.01426741, ..., -0.04875181, 0.13051308, 0. ]], ..., [[ 0. , 0.0855516 , 0.13597267, ..., 0.16457852, 0.04648458, 0.03031205], [ 0.0855516 , 0. , -0.02231855, ..., -0.01696728, 0.06332429, -0.05170068], [ 0.13597267, -0.02231855, 0. , ..., -0.14505349, -0.05634801, 0.06301302], ..., [ 0.16457852, -0.01696728, -0.14505349, ..., 0. , -0.09211864, 0.03218952], [ 0.04648458, 0.06332429, -0.05634801, ..., -0.09211864, 0. , 0.56874401], [ 0.03031205, -0.05170068, 0.06301302, ..., 0.03218952, 0.56874401, 0. ]], [[ 0. , 0.00369714, 0.01803972, ..., 0.02636135, 0.01498399, 0.00107738], [ 0.00369714, 0. , -0.04215888, ..., 0.08585435, 0.01525622, -0.0044203 ], [ 0.01803972, -0.04215888, 0. , ..., 0.00435903, -0.04385825, 0.01943377], ..., [ 0.02636135, 0.08585435, 0.00435903, ..., 0. , 0.08473985, 0.14461459], [ 0.01498399, 0.01525622, -0.04385825, ..., 0.08473985, 0. , 0.1207058 ], [ 0.00107738, -0.0044203 , 0.01943377, ..., 0.14461459, 0.1207058 , 0. ]], [[ 0. , 0.02612483, 0.03413983, ..., 0.06152281, -0.02745578, -0.01738546], [ 0.02612483, 0. , -0.04195744, ..., 0.01392757, -0.07636841, 0.00512999], [ 0.03413983, -0.04195744, 0. , ..., -0.00615086, 0.00395361, 0.02567149], ..., [ 0.06152281, 0.01392757, -0.00615086, ..., 0. , 0.01919661, 0.07781511], [-0.02745578, -0.07636841, 0.00395361, ..., 0.01919661, 0. , 0.16214421], [-0.01738546, 0.00512999, 0.02567149, ..., 0.07781511, 0.16214421, 0. ]]], shape=(1009, 111, 111)), 'site': array(['PITT', 'PITT', 'PITT', ..., 'SBL', 'MAX_MUN', 'MAX_MUN'], shape=(1009,), dtype='<U8')}

它其实是一个被处理过后的数据集，比如说ABIDE数据集。这个abide.npy最后一个属性是site，它是一个一维数组，有1009个元素。为什么是1009呢，这代表这些数据可以分成1009组，它的site有时候是相同的，这代表可能在一个地方收集了多组数据。你比如说这个PITT吧，它就可能代表匹兹堡。 这个label也是一个很简单的属性，给数据打标签嘛，这个数据集都是ABIDE数据集的内容，所以没有打标签，也可能是后续要划分实验组什么的打标签。 我总算知道这个coor是什么了，我之前看过一篇论文，里面提到了皮尔逊积差相关系数，是计算两组数据之间线性关系的。比如此处吧，coor的shape=(1009, 111, 111)，意思就是说有1009个二维数组，依次分别是[0][0],[0][1],[0][2],…,[0][1008];然后是[1][0],[1][1],…,[1][1008],不断递进，最后是[1008][0],[1008][1],…,[1008][1008] [ 0. , 0.97663709, 0.82530072, …, 0.13740499, 0.35407144, 0.08548723],你看这个吧，0代表第一组数据和第二组数据之间的皮尔逊积差相关系数是0。这个肯定是处理过后的数据，因为一组数据和自己的相关性为1，它这里是0，肯定是去除了自相关。再看 0.97663709，这代表第一组数据和第二组数据的相关性为0.97663709，相关性快接近1了，这说明相关性很强。还有这个0.82530072，它代表第一组数据和第三组数据之间的相关性也很高了。中间有一堆的省略号，这个很有意思，这是因为数据太多了，所以它显示的时候把中间这部分省略掉了。这个0.13740499代表这两组数据之间的相关性很弱了。 我说既然有了corr，也就是皮尔逊积差相关系数的矩阵，为什么还要有pcorr，原来pcorr是部分相关系数的矩阵。 我懂了，我之前在论文里面一直看别人是怎么处理数据的，你像这个

'timeseires': array([[[ -4.671351, -12.931799, -20.238678, ..., -4.6999 , -2.881232, -5.373659], [ -6.610331, -10.146871, -11.706947, ..., -8.545325, -4.824956, -3.549801], [ 6.539966, -3.924838, -22.075667, ..., -10.810907, -9.507657, -11.782674], ..., [ 23.815056, 14.159346, -4.235137, ..., 2.318333, -0.419391, -6.08656 ], [ 29.358294, 29.569822, 12.97839 , ..., -35.478521, -32.452544, -38.755226], [ 12.700474, 10.523634, -1.103658, ..., 16.980012, 18.994251, 7.554179]], [[ 15.055096, 14.30167 , 8.35954 , ..., -2.320052, 5.738047, 18.929523], [ -2.650435, -5.070064, -6.582098, ..., -6.500024, -6.520322, -2.209336], [ 7.958244, 7.113133, 3.146248, ..., -7.816352, 0.160806, 12.985252], ..., [ 0.216612, -7.946295, -14.338741, ..., -2.661149, -1.293657, 4.052661], [ 0.595963, -1.462514, -3.352045, ..., -7.601621, -1.38041 , 7.066522], [ -1.754612, -2.342497, -2.100473, ..., -4.888682, 0.817308, 6.089231]], [[ 10.699614, 12.640328, 9.107096, ..., 25.303575, 14.631509, -2.431361], [ 8.946028, 11.992252, 10.768465, ..., 17.12695 , 4.901006, -9.559137], [ 12.502747, 17.729441, 17.117685, ..., 13.455708, -13.292004, -35.746319], ..., [-13.968082, -18.558227, -20.739389, ..., 30.461445, 48.840466, 47.846905], [ 7.775516, 7.945796, 4.77154 , ..., 35.356228, 42.096015, 35.434606], [ 3.752828, 5.027397, 4.62332 , ..., 22.339197, 34.736423, 33.95411 ]], ..., [[ -9.594532, -18.276965, -12.977462, ..., 22.482481, 9.076281, -3.665357], [ -1.753394, -3.048763, -0.66247 , ..., 3.479453, 6.685637, 5.947072], [ -7.053816, -4.420122, 6.336983, ..., 23.162804, 14.313564, 2.746504], ..., [ 14.337799, 18.14827 , -0.895313, ..., 37.246694, 58.276117, 34.074897], [-15.100426, -22.201254, -24.41196 , ..., 60.251682, 66.325714, 32.992202], [-13.52109 , -18.166892, -16.496382, ..., 38.908534, 41.846779, 15.183713]], [[ -4.960212, -10.467057, -2.350238, ..., 8.175621, -3.594708, -13.050947], [ -2.188337, -5.102677, -1.05976 , ..., 3.500581, 5.308049, -0.665579], [ -7.689325, -4.299846, 4.479113, ..., 7.190843, -6.158385, -10.379174], ..., [ 4.838299, -1.461336, 3.449214, ..., 22.214853, 10.277305, -20.902231], [ -9.16024 , -6.62222 , 5.643113, ..., 1.50843 , -7.206676, -21.635724], [ -7.149388, -5.05326 , 5.05269 , ..., 12.605102, 7.582936, -10.857408]], [[ -8.999973, -8.265457, 0.224464, ..., 2.243862, -2.492217, -13.048096], [ -1.115053, 0.311383, -0.91307 , ..., 3.98221 , 1.695672, -5.227182], [ 2.39671 , 2.200741, 5.486765, ..., 13.607921, 7.737062, -13.571547], ..., [-24.200021, -15.751269, 3.40317 , ..., 3.005727, 3.171528, -1.583004], [-12.074557, -7.229352, -1.982399, ..., 4.310723, -9.359018, -3.962187], [ -7.750716, -6.153427, -2.874191, ..., 4.213236, -2.779235, -1.473658]]], shape=(1009, 111, 100)),

它记录的是1009个时刻，然后大脑分为111个脑区，在固定的时刻，固定的脑区，还有不同的测量维度之类的，我感觉大脑真的是很难估量的。 2 现在我看懂这个代码是什么意思了

final_fc = data["timeseires"] final_pearson = data["corr"] labels = data["label"]

npy文件分为五部分数据嘛，只用到了其中的三部分，然后分别赋值给final_fc，final_pearson,labels 3

_, _, timeseries = final_fc.shape _, node_size, node_feature_size = final_pearson.shape

这里就是要把1009个时间点赋值给timeseries，node_size为111，代表111个脑区维度，100代表不同的维度啊之类的 4