Deber de comparacion del tiempo de ordenamiento entre bubble sort e i…

Programs/Perceptron_OOP/Clase_perceptron.py

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+class perceptron:
+
+    def insert_matriz(self):
+        # La mas sencilla e intuitiva
+        self.numero_filas=2
+        numero_columnas=2
+        matriz = []
+        for i in range(numero_filas):
+            matriz.append([])
+            for j in range(numero_columnas):
+                print("Ingrese elemento:")
+                matriz[i].append(input())
+        print(matriz)
+
+    def insert_arreglo(self):
+        arr = []
+        self.numero_filas = 2
+        for i in range(self.numero_filas):
+            print("Ingrese elemento:")
+            arr.append(input())
+        print(arr)
+    def learn1(self):
+        return learn_rate=0.1
+    def learn2(self):
+        return learn_rate=0.01
+    def learn3(self):
+        return learn_rate=0.001
+    def numbers_learn(self,argument):
+        switcher = {
+            0: self.learn1,
+            1: self.learn2,
+            2: self.learn3,
+        }
+        return switcher.get(argument,lambda: "Invalid")
+
+    def signo(self,net):
+        fsigno = [1. if elem >=0 else -1. for elem in net]
+        return fsigno
+    def paso(self,net):
+        fpaso = [1. if elem >= 0 else 0. for elem in net]
+        return fpaso
+    def sigmoide(self,net):
+        fsigmoide=1/(1+np.exp(elem) for elem in net)
+        return fsigmoide
+    def tanhip(self,net):
+        ftanhip=((np.exp(elem)-np.exp(-elem))/(np.exp(elem)+np.exp(-elem)) for elem in net )
+        return ftanhip
+
+    def funcion(self,argumento):
+        switcher = {
+            1: self.signo(),
+            2: self.paso(),
+            3: self.sigmoide(),
+            4: self.tanhip(),
+        }
+    # Get the function from switcher dictionary
+        return switcher.get(argumento, lambda: "Invalid")