From f5d96de04f1cc3a846ef93eec68a81484dfa3ee4 Mon Sep 17 00:00:00 2001
From: tnbeats <tnbeatsprod@gmail.com>
Date: Sun, 19 Nov 2023 14:03:26 +0100
Subject: [PATCH] 22211572
---
Reinforcement_Learning/Perceptrons.py | 10 ++++-
Reinforcement_Learning/PerceptronsSGD.py | 47 ++++++++++++++++++++++++
2 files changed, 55 insertions(+), 2 deletions(-)
create mode 100644 Reinforcement_Learning/PerceptronsSGD.py
diff --git a/Reinforcement_Learning/Perceptrons.py b/Reinforcement_Learning/Perceptrons.py
index 07325c1..d3c37c2 100644
--- a/Reinforcement_Learning/Perceptrons.py
+++ b/Reinforcement_Learning/Perceptrons.py
@@ -8,6 +8,10 @@ TESTDATA = 1000
T_NUMBER = 0
+def sigmoid(val):
+ return 1 / (1 + np.exp(-val))
+
+
def linear_act(val):
return val
@@ -31,7 +35,8 @@ class Perceptron:
ix = np.insert(teach_data[j][i].ravel(), 0, 1)
RI = self.activation(ix.dot(self.weights))
if RI != T:
- delta = ETA * (T - self.activation(ix.dot(self.weights))) * ix
+ delta = ETA * \
+ (T - self.activation(ix.dot(self.weights))) * ix
self.weights = self.weights + delta
# print(self.weights[0], self.weights[1], self.weights[2], self.weights[3], self.weights[4], self.weights[5], self.weights[6])
if np.linalg.norm(old_weights - self.weights) == 0.00:
@@ -45,5 +50,6 @@ class Perceptron:
for number in range(len(test_data)):
for sample in test_data[number]:
ix = np.insert(sample.ravel(), 0, 1)
- res[number] = res[number] + (self.activation(ix.dot(self.weights)))
+ res[number] = res[number] + \
+ (self.activation(ix.dot(self.weights)))
return res
diff --git a/Reinforcement_Learning/PerceptronsSGD.py b/Reinforcement_Learning/PerceptronsSGD.py
new file mode 100644
index 0000000..805f222
--- /dev/null
+++ b/Reinforcement_Learning/PerceptronsSGD.py
@@ -0,0 +1,47 @@
+import numpy as np
+import Training_data
+
+rng = np.random.default_rng(123)
+TEACHDATA = 10000
+TESTDATA = 1000
+# ETA = 0.5
+T_NUMBER = 0
+
+
+def sigmoid(val):
+ return 1 / (1 + np.exp(-val))
+
+
+def sigmoid_derivative(val):
+ return sigmoid(val) * (1 - sigmoid(val))
+
+
+class PerceptronSGD:
+ def __init__(self, input_count, activation=sigmoid, activation_derivative=sigmoid_derivative):
+ self.input_count = input_count
+ self.activation = activation
+ self.activation_derivative = activation_derivative
+ self.weights = rng.random(input_count + 1)
+
+ def train(self, ETA, NUM_EPOCHS):
+ teach_data = Training_data.make_testset(TEACHDATA)
+ for epoch in range(NUM_EPOCHS):
+ for i in range(TEACHDATA):
+ for j in rng.permutation(len(teach_data)):
+ T = 1 if j == T_NUMBER else 0
+ ix = np.insert(teach_data[j][i].ravel(), 0, 1)
+ z = ix.dot(self.weights)
+ RI = self.activation(z)
+ error = T - RI
+ delta = ETA * error * self.activation_derivative(z) * ix
+ self.weights += delta
+ return self.weights
+
+ def test(self):
+ test_data = Training_data.make_testset(TESTDATA)
+ res = [0 for _ in range(len(test_data))]
+ for number in range(len(test_data)):
+ for sample in test_data[number]:
+ ix = np.insert(sample.ravel(), 0, 1)
+ res[number] += (self.activation(ix.dot(self.weights)))
+ return res
--
GitLab