From 54598b3af73ef375dff5edfb24fdddd6387fa751 Mon Sep 17 00:00:00 2001
From: Michael Mutote <130656746+mr1Michael@users.noreply.github.com>
Date: Thu, 16 Nov 2023 18:24:31 +0100
Subject: [PATCH] 22202956 - Threshold Perceptron completed, skeleton of linear
created
---
Reinforcement_Learning/Perceptrons.py | 45 ++++++++--------------
Reinforcement_Learning/Solution_Testing.py | 19 +++++++++
2 files changed, 36 insertions(+), 28 deletions(-)
create mode 100644 Reinforcement_Learning/Solution_Testing.py
diff --git a/Reinforcement_Learning/Perceptrons.py b/Reinforcement_Learning/Perceptrons.py
index 76aeb17..07325c1 100644
--- a/Reinforcement_Learning/Perceptrons.py
+++ b/Reinforcement_Learning/Perceptrons.py
@@ -2,10 +2,14 @@ import numpy as np
import Training_data
rng = np.random.default_rng(123)
-TEACHDATA = 90000
-TESTDATA = 1
+TEACHDATA = 10000
+TESTDATA = 1000
# ETA = 0.5
-T_NUMBER = 3
+T_NUMBER = 0
+
+
+def linear_act(val):
+ return val
def threshold(val):
@@ -16,45 +20,30 @@ class Perceptron:
def __init__(self, input_count, activation=threshold):
self.input_count = input_count
self.activation = activation
- self.weights = rng.random(input_count + 1) * 0.3
+ self.weights = rng.random(input_count + 1)
def train(self, ETA):
teach_data = Training_data.make_testset(TEACHDATA)
- for i in rng.permutation(TEACHDATA):
+ for i in range(TEACHDATA):
old_weights = np.copy(self.weights)
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)
- # for wx in rng.permutation(len(ix)):
RI = self.activation(ix.dot(self.weights))
- if not RI == T:
+ if RI != T:
delta = ETA * (T - self.activation(ix.dot(self.weights))) * ix
self.weights = self.weights + delta
- if np.linalg.norm(old_weights - self.weights) <= 0.00000000001:
+ # 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:
return self.weights
return self.weights
def test(self):
test_data = Training_data.make_testset(TESTDATA)
# print(test_data)
- res = []
- for number in test_data:
- # print(number[0])
- ix = np.insert(number[0].ravel(), 0, 1)
- res.append(self.activation(ix.dot(self.weights)))
+ 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] = res[number] + (self.activation(ix.dot(self.weights)))
return res
-
-
-def test_function():
- input_count = 20 # the number of elements in each input array
- ETA_list = [0.0001, 0.001, 0.01, 0.1, 0.5, 1, 1.5, 2] # the list of values for ETA
- results = [] # the list of results for each ETA value
- for ETA in ETA_list:
- p = Perceptron(input_count) # create a perceptron instance
- p.train(ETA) # train the perceptron with the given ETA value
- output = p.test() # test the perceptron on a single test pattern
- results.append((ETA, output)) # append the ETA value and the output to the results list
- return results # return the results list
-
-
-print(test_function()) # print the results list
diff --git a/Reinforcement_Learning/Solution_Testing.py b/Reinforcement_Learning/Solution_Testing.py
new file mode 100644
index 0000000..334b49e
--- /dev/null
+++ b/Reinforcement_Learning/Solution_Testing.py
@@ -0,0 +1,19 @@
+import Perceptrons
+
+
+def test_function(ETA):
+ input_count = 20
+
+ results = []
+ p = Perceptrons.Perceptron(input_count)
+ p.train(ETA)
+ output = p.test()
+ results.append((ETA, output))
+ return results
+
+
+for ETA in ([0.05, 0.1, 0.2, 0.4, 0.75, 1, 2, 5]): # the list of values for ETA
+ for i in range(5):
+ res = test_function(ETA)
+ print(res) # print the results list
+ print("\n\n")
--
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