Merge pull request #106 from frmatias/master

Neural Network - Perceptron

Graphs/Breadth_First_Search.py

@@ -7,6 +7,41 @@ class GRAPH:
 
 
     def show(self):
+
+        for i in self.graph:
+            for j in i:
+                print(j, end=' ')
+            print(' ')
+    def bfs(self,v):
+
+        visited = [False]*self.vertex
+        visited[v - 1] = True
+        print('%d visited' % (v))
+
+        queue = [v - 1]
+        while len(queue) > 0:
+            v = queue[0]
+            for u in range(self.vertex):
+                if self.graph[v][u] == 1:
+                    if visited[u]== False:
+                        visited[u] = True
+                        queue.append(u)
+                        print('%d visited' % (u +1))
+            queue.pop(0)
+
+g = Graph(10)
+
+g.add_edge(1,2)
+g.add_edge(1,3)
+g.add_edge(1,4)
+g.add_edge(2,5)
+g.add_edge(3,6)
+g.add_edge(3,7)
+g.add_edge(4,8)
+g.add_edge(5,9)
+g.add_edge(6,10)
+g.bfs(4)
+=======
         print self.graph
 
     def add_edge(self, i, j):

Graphs/Graph_list.py

@@ -15,7 +15,7 @@ class Graph:
 
 
 
-g = Graph(5)
+g = Graph(100)
 
 g.add_edge(1,3)
 g.add_edge(2,3)
@@ -23,5 +23,6 @@ g.add_edge(3,4)
 g.add_edge(3,5)
 g.add_edge(4,5)
 
+
 g.show()
 

Graphs/Graph_matrix.py

@@ -18,13 +18,12 @@ class Graph:
 
 
 
-g = Graph(5)
+g = Graph(100)
 
-g.add_edge(1,3)
+g.add_edge(1,4)
-g.add_edge(2,3)
+g.add_edge(4,2)
-g.add_edge(3,4)
-g.add_edge(3,5)
 g.add_edge(4,5)
-
+g.add_edge(2,5)
+g.add_edge(5,3)
 g.show()
 

Neural_Network/perceptron.py

@@ -0,0 +1,123 @@
+'''
+
+	Perceptron
+	w = w + N * (d(k) - y) * x(k)
+
+	Using perceptron network for oil analysis,
+	with Measuring of 3 parameters that represent chemical characteristics we can classify the oil, in p1 or p2
+	p1 = -1
+	p2 = 1
+
+'''
+
+import random
+
+
+class Perceptron:
+    def __init__(self, sample, exit, learn_rate=0.01, epoch_number=1000, bias=-1):
+        self.sample = sample
+        self.exit = exit
+        self.learn_rate = learn_rate
+        self.epoch_number = epoch_number
+        self.bias = bias
+        self.number_sample = len(sample)
+        self.col_sample = len(sample[0])
+        self.weight = []
+
+    def trannig(self):
+        for sample in self.sample:
+            sample.insert(0, self.bias)
+
+        for i in range(self.col_sample):
+           self.weight.append(random.random())
+
+        self.weight.insert(0, self.bias)
+
+        epoch_count = 0
+
+        while True:
+            erro = False
+            for i in range(self.number_sample):
+                u = 0
+                for j in range(self.col_sample + 1):
+                    u = u + self.weight[j] * self.sample[i][j]
+                y = self.sign(u)
+                if y != self.exit[i]:
+
+                    for j in range(self.col_sample + 1):
+
+                        self.weight[j] = self.weight[j] + self.learn_rate * (self.exit[i] - y) * self.sample[i][j]
+                    erro = True
+            #print('Epoch: \n',epoch_count)
+            epoch_count = epoch_count + 1
+            # if you want controle the epoch or just by erro
+            if erro == False:
+                print('\nEpoch:\n',epoch_count)
+                print('------------------------\n')
+            #if epoch_count > self.epoch_number or not erro:
+                break
+
+    def sort(self, sample):
+        sample.insert(0, self.bias)
+        u = 0
+        for i in range(self.col_sample + 1):
+            u = u + self.weight[i] * sample[i]
+
+        y = self.sign(u)
+
+        if  y == -1:
+            print('Sample: ', sample)
+            print('classification: P1')
+        else:
+            print('Sample: ', sample)
+            print('classification: P2')
+
+    def sign(self, u):
+        return 1 if u >= 0 else -1
+
+
+samples = [
+    [-0.6508, 0.1097, 4.0009],
+    [-1.4492, 0.8896, 4.4005],
+    [2.0850, 0.6876, 12.0710],
+    [0.2626, 1.1476, 7.7985],
+    [0.6418, 1.0234, 7.0427],
+    [0.2569, 0.6730, 8.3265],
+    [1.1155, 0.6043, 7.4446],
+    [0.0914, 0.3399, 7.0677],
+    [0.0121, 0.5256, 4.6316],
+    [-0.0429, 0.4660, 5.4323],
+    [0.4340, 0.6870, 8.2287],
+    [0.2735, 1.0287, 7.1934],
+    [0.4839, 0.4851, 7.4850],
+    [0.4089, -0.1267, 5.5019],
+    [1.4391, 0.1614, 8.5843],
+    [-0.9115, -0.1973, 2.1962],
+    [0.3654, 1.0475, 7.4858],
+    [0.2144, 0.7515, 7.1699],
+    [0.2013, 1.0014, 6.5489],
+    [0.6483, 0.2183, 5.8991],
+    [-0.1147, 0.2242, 7.2435],
+    [-0.7970, 0.8795, 3.8762],
+    [-1.0625, 0.6366, 2.4707],
+    [0.5307, 0.1285, 5.6883],
+    [-1.2200, 0.7777, 1.7252],
+    [0.3957, 0.1076, 5.6623],
+    [-0.1013, 0.5989, 7.1812],
+    [2.4482, 0.9455, 11.2095],
+    [2.0149, 0.6192, 10.9263],
+    [0.2012, 0.2611, 5.4631]
+
+]
+
+exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1]
+
+network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
+
+network.trannig()
+
+while True:
+    sample = []
+    for i in range(3):
+        sample.insert(i, float(input('value: ')))
+    network.sort(sample)