From a1c7fe1b6d95e2560e62c12453da287d36d4a714 Mon Sep 17 00:00:00 2001
From: Charles Cabergs <me@cacharle.xyz>
Date: Sat, 29 Aug 2020 17:13:57 +0200
Subject: Removed bloat, Added logreg_train

---
 src/model.py | 69 ------------------------------------------------------------
 1 file changed, 69 deletions(-)
 delete mode 100644 src/model.py

(limited to 'src/model.py')

diff --git a/src/model.py b/src/model.py
deleted file mode 100644
index 47a660c..0000000
--- a/src/model.py
+++ /dev/null
@@ -1,69 +0,0 @@
-class Model:
-    def __init__(self, weights_filename='weights'):
-        self.weights_filename = weights_filename
-
-    def train(self, xs, ys, alpha=1, epoch=1000):
-        for _ in range(epoch):
-            theta = theta - alpha * self.gradient(xs, ys)
-
-    def train_against(self, xs, ys, theta, one, alpha, epoch):
-        ys_ally = ys.copy()
-        ys_ally[ys == one] = 0
-        ys_ally[ys != one] = 1
-        return gradient_descent(xs, ys_ally, theta, alpha, epoch)
-
-    def train_thetas(xs, ys, theta, alpha=1, epoch=1000):
-        thetas = []
-        for i in np.unique(ys):
-            thetas.append(train_against(xs, ys, theta, i, alpha, epoch))
-            return thetas
-
-    def gradient(self, xs, ys):
-        return np.array([self.partial(xs, ys, i) for i in range(len(self.theta))])
-
-    def partial(self, xs, ys, theta_j):
-        total = 0
-        for x_i, y_i in zip(xs, ys):
-            temp = self.hypothesis(x_i) - y_i
-            if theta_j != 0:
-                temp *= x_i[theta_j - 1]
-                total += temp
-                return total / len(xs)
-
-    def predict(self, x):
-        return 1 if self.hypothesis(x) >= 0.5 else 0
-
-    def hypothesis(self, x):
-        return self._sigmoid(x.dot(self.theta))
-
-    def logloss(self, x, y):
-        if y == 1:
-            return -np.ln(self.hypothesis(x))
-        elif y == 0:
-            return -np.ln(1 - self.hypothesis(x))
-        else:
-            raise "y != 1 and y != 0"
-
-    def cost(self, xs, ys):
-        return sum([self.logloss(x, y) for x, y in zip(xs, ys)]) / len(xs)
-
-    def _sigmoid(self, x):
-        return 1 / (1 + np.exp(-x))
-
-    def _normalize(self, x):
-        return (x - x.min()) / (x.max() - x.min())
-
-    def _read_weights(self):
-        try:
-            with open(self.weights_filename, 'r') as file:
-                self.weights = np.array(
-                    [float(s) for s in file.read().strip().split(',')])
-        except IOError:
-            raise 'Couldn\'t read weights file at: {}'.format(self.weights_filename)
-
-    def _write_weights(self):
-        try:
-            with open(self.weights_filename, 'w') as file:
-                file.write(','.join([str(w) for w in self.weights])
-        except IOError:
-            raise 'Couldn\'t write weights file at: {}'.format(self.weights_filename)
-- 
cgit