我有一堆3x2矩阵，让我们说777个矩阵，以及大小为3的右侧。对于每个矩阵，我想知道最小平方的解决方案，所以我和＃39;正在做
numpy.linalg.lstsq(A, b)
这有效，但速度很慢。我更愿意一次性计算所有解决方案，但是
numpy.linalg.linalg.LinAlgError: 3-dimensional array given. Array must be two-dimensional
我正在
numpy.linalg.lstsq有关如何广播RewriteEngine On
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的任何提示？
答案 0 :(得分：2)
可以利用//Create javascript arrays with the values and labels, replace this with code to read from the database/API/etc.
var array_1_values = [100, 120, 180, 200, 90]; //these are the values of the first line
var array_2_values = [20, 35, 65, 125, 245]; //these are the values of the second line
var array_labels = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']; //these are the labels that will appear at the bottom of the chart
//create a prototype multi-dimensional array
var data_chart1 = {
labels: [],
series: [
[],
[]
]
};
//populate the multi-dimensional array
for (var i = 0; i < array_1_values.length; i += 1)
{
data_chart1.series[0].push(array_1_values[i])
data_chart1.series[1].push(array_2_values[i])
data_chart1.labels.push(array_labels[i])
}
//set the size of chart 1
var options_chart1 = {
width: '300px',
height: '200px'
};
//create chart 1
new Chartist.Line('#chart1', data_chart1, options_chart1);
是A = U \Sigma V^T的奇异值分解，
A是x = V \Sigma^+ U^T b
的最小二乘解。 SVD is broadcasted in numpy。现在只需要稍微摆弄一下Ax = b即可完成任务：
einsum