1 files changed, 68 insertions, 0 deletions
diff --git a/bin/model.py b/bin/model.py
new file mode 100755
index 0000000..c44e0a6
--- /dev/null
+++ b/bin/model.py
@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+
+from typing import List, Tuple
+from sklearn import linear_model
+import tqdm
+import scipy
+import sqlite3
+import argparse
+import numpy as np
+
+class matrix_builds_inputpnames:
+    independent_variables: scipy.sparse.coo_array
+    dependent_variable: np.ndarray
+
+    def __init__(self, db: str) -> None:
+        row: List[int] = []
+        col: List[int] = []
+        data: List[int] = []
+
+        con = sqlite3.connect(db)
+        cur = con.cursor()
+
+        builds = self.table_fetch(cur, '''
+            SELECT ROWID, drv_id, duration FROM builds_cleaned
+        ''')
+        self.dependent_variable = np.array([ [ row[1] ] for row in tqdm.tqdm(builds) ])
+
+        for build in tqdm.tqdm(builds):
+            inputpnames = self.table_fetch(cur, '''
+                SELECT drv_id, pname_id FROM input_pnames
+                WHERE input_pnames.drv_id = ?
+            ''', (build[1],) )
+
+            for inputpname in inputpnames:
+                row.append(build[0] - 1)
+                col.append(inputpname[1])
+                data.append(1)
+
+        np_row = np.array(row)
+        np_col = np.array(col)
+        np_data = np.array(data)
+        self.independent_variables = scipy.sparse.coo_array((np_data, (np_row, np_col)))
+
+        con.commit()
+        con.close()
+
+    @staticmethod
+    def table_fetch(cur: sqlite3.Cursor, query: str, args: Tuple = ()) -> List:
+        query_exec = cur.execute(query, args)
+        return query_exec.fetchall()
+
+def args_get():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-d', '--db', required=True, help="path to sqlite database containing input_pnames and builds")
+    parser.add_argument('-t', '--test', type=int, required=True, help="row to test against")
+    return parser.parse_args()
+
+if __name__ == '__main__':
+    args = args_get()
+    matrix = matrix_builds_inputpnames(args.db)
+
+    regr = linear_model.LinearRegression()
+    regr.fit(matrix.independent_variables, matrix.dependent_variable)
+    pred = regr.predict([matrix.independent_variables.toarray()[args.test]])
+
+    print(f'prediction  : {pred[0][0]}')
+    print(f'og duration : {matrix.dependent_variable[args.test][0]}')
+