logregress: added docs

Author: Adwaith-Rajesh

docs/ml/logregress.md

@@ -48,4 +48,93 @@ When you have the final values from your derivative calculation, you can use it
 
 ## The code
 
-Coming soon
+The data used here is the [Breast Cancer Wisconsin (Diagnostic) Data Set](https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data) which has bee modified to look like [this](https://gitlab.com/adwaithrajesh/linear-ml-test/-/blob/main/data/bcancer.csv), where we
+don't have id's and M=0, and B=1
+
+```c
+#define INCLUDE_MAT_CONVERSIONS
+#include "ds/mat.h"
+#include "ml/logisticregress.h"
+#include "model/metrics.h"
+#include "model/train_test_split.h"
+#include "parsers/csv.h"
+
+int main(void) {
+    CSV *csv_reader = csv_init(569, 31, ',');
+    csv_parse(csv_reader, "data/bcancer.csv");
+
+    Mat *X = csv_get_mat_slice(csv_reader, (Slice){1, 31});
+    Mat *Y = csv_get_mat_slice(csv_reader, (Slice){0, 1});
+    Mat *X_train, *X_test, *Y_train, *Y_test;
+
+    train_test_split(X, Y, &X_train, &X_test, &Y_train, &Y_test, 0.3, 101);
+
+    logregress_set_max_iter(2000);
+    LogisticRegressionModel *model = logregress_init();
+    logregress_fit(model, X_train, Y_train);
+
+    // printf("prediction: %lf\n", logregress_predict(model, (double[]){15.22, 30.62, 103.4, 716.9, ... , 0}, 30));
+    Array *preds = logregress_predict_many(model, X_test);
+    Array *true = mat_get_col_arr(Y_test, 0);
+
+    logregress_print(model);
+
+    printf("confusion matrix: \n");
+    Mat *conf_mat = model_confusion_matrix(true, preds);
+    mat_print(conf_mat);
+
+    arr_free(true);
+    arr_free(preds);
+    logregress_free(model);
+    mat_free_many(7, X, Y, X_test, X_train, Y_test, Y_train, conf_mat);
+    csv_free(csv_reader);
+}
+```
+
+```console
+LogisticRegressionModel(bias: 0.5159147, loss: -12.4263621, weights: 0x5556e8a732c0)
+weights:
+1546.6922009
+1139.6829595
+8552.1648900
+2522.0044946
+11.8724211
+-19.3345598
+-44.9646156
+-18.4984994
+23.8378678
+10.1676564
+0.2338315
+103.3839701
+-139.7864354
+-4498.8563443
+0.2662770
+-6.5798244
+-8.6158697
+-1.6938180
+1.6508702
+-0.3857419
+1650.7843571
+1445.0283208
+8312.7672485
+-4024.9280673
+13.2972726
+-72.4527931
+-111.8298475
+-26.6204266
+28.0612275
+5.4099162
+confusion matrix:
+  57.00   10.00
+  2.00   101.00
+```
+
+Now, what does the confusion matrix generated by sklean look like.
+
+```python
+array([[ 59,   7],
+       [  3, 102]])
+```
+
+we are pretty close...
+checkout the python implementation [here](https://gitlab.com/adwaithrajesh/linear-ml-test/-/blob/main/notebooks/log.ipynb)