"First, we will generate highly correlated data, containing a sample X (multidim) and a target y (one dim).\n",
"\n",
"#### Data generation first function: generate_data\n",
"We write a function for this.\n",
"Its parameters are :\n",
"- n_samples the number of samples\n",
...
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@@ -58,7 +58,25 @@
"- For the other dimensions of X, noted i, the value will be calculated as follow :\n",
" - We generate a number from a normal law N(i / 2, 1)\n",
" - We add it to the value of the first column\n",
"- For Y, we select 2 over 3 values of X and we sum them"
"- For Y, we select 2 over 3 values of X and we sum them\n",
"\n",
"#### Data generation second function: generate_data_2\n",
"We have written a second function, which generate another highly correlated data set in order to compare our results.\n",
"Its parameters are\n",
"- n_samples the number of samples \n",
"- n_features the number of features in X\n",
"and the outputs X and y\n",
"\n",
"For this purpose, we proceed in 4 steps:\n",
"\n",
"- we generate samples of a geometric law of parameter p = 0.5, these samples are stored in the first column of X\n",
"- for the other columns of X we do\n",
" - we generate randomly a parameter p between 0 and 1\n",
" - we generate samples of a geometric law of parameter p\n",
" - we add this samples to the sum of the previous column\n",
" \n",
"At the end, we have the matrix X where each column `Xi` is a sum of a samples generated from a geometric law and the previous columns `X0+...+Xi-1`.\n",
"We generate `y` as the mean of `X` on the axis 1."
