2017/10/14

Author: AsaEwing

develop/__init__.py

@@ -0,0 +1 @@
+from develop import dev_ANNs

develop/dev_ANNs/__init__.py

@@ -0,0 +1 @@
+from develop.dev_ANNs.neuron.neuron import neuron

develop/dev_ANNs/neuron/__init__.py

@@ -0,0 +1,165 @@
+#!/usr/local/bin/python3
+# -*-coding:utf-8 -*-
+"""
+# ==================================================
+# Name              : neuron
+# Action            : module for neuron
+# Explanation       : make a module for neuron
+# Self Class used   : neuron
+# Note              :
+# ==================================================
+"""
+
+from module_for_all import log_thing
+import numpy as np
+import random
+
+from module_for_all.log_thing import Basic
+
+
+class neuron_original(log_thing.Timer):
+    """
+* ==================================================
+* Self Class    : neuron
+* Extends       : log_thing.Timer
+* Explanation   : 基本單一 neuron 設置
+* ==================================================
+    """
+
+    def __init__(self, inputX: np.array, outputY: float, weight: np.array, bias: float):
+        """
+        1. need to super, send a class object
+        2. make neuron's basic setting
+        """
+        super().__init__(self.__class__)
+
+        self.inputX = inputX
+        self.outputY = outputY
+        self.weight = weight
+        self.bias = bias
+        return
+
+    def nextInput(self):
+        print("### 1")
+        return
+
+    def nextWeight(self):
+        return
+
+    def nextBias(self):
+        return
+
+
+class neuron(neuron_original):
+    """
+* ==================================================
+* Self Class    : neuron
+* Extends       : neuron_original
+* Explanation   : 包裝 neuron. 多上了數值檢測, 分配等等
+* ==================================================
+    """
+
+    def __init__(self, inputArray: list, outputCorrect: float, weight: list = None, bias: float = None, isList=False):
+        """
+        1. need to super, to make neuron
+        2. used to check data
+        """
+
+        self.ors_inArray = inputArray
+        self.ors_outCorrect = outputCorrect
+
+        self.inArray = None
+        self.outCorrect = None
+
+        self.isList = isList
+
+        if weight is None:
+            self.weight = random.uniform(0, 1)
+            print(self.weight)
+        else:
+            self.weight = weight
+
+        if bias is None:
+            self.bias = random.uniform(0, 1)
+            print(self.bias)
+        else:
+            self.bias = bias
+
+        self.checkAllVar()
+
+        super().__init__(self.inArray, self.outCorrect, self.weight, self.bias)
+        return
+
+    def checkAllVar(self):
+
+        self.inArray = self.checkArray("ors_inArray", self.ors_inArray)
+        self.outCorrect = self.checkFloat("ors_outCorrect", self.ors_outCorrect)
+
+        return
+
+    def checkArray(self, varName, tmpVar):
+
+        if type(tmpVar) == list:
+            tmpVar = np.array(tmpVar)
+
+            if not self.isList:
+                print("need to check, is type of \"" + varName + "\" list?")
+
+        elif type(tmpVar) == np.array:
+            return tmpVar
+
+        else:
+            print("need to check, is type of \"" + varName + "\" list or np.array!")
+            return None
+
+        return tmpVar
+
+    def checkFloat(self, varName, tmpVar):
+
+        if type(tmpVar) == int:
+            tmpVar = float(tmpVar)
+
+        elif type(tmpVar) == complex:
+            print("need to check, type of \"" + varName + "\" need to int or float!")
+            return None
+
+        elif type(tmpVar) == float:
+            return tmpVar
+
+        else:
+            print("need to check, type of \"" + varName + "\" need to int or float!")
+            return None
+
+        return tmpVar
+
+    def nextInput(self):
+        super().nextInput()
+        print("### 2")
+        return
+
+
+"""
+x_all = sum(i=1 to n, weight_i * x_i) - bias
+y_sigmoid = 1/(1+exp(-x_all)) or 1/(1+5exp(-5*x_all))
+
+diffDelta(p) = y_correct(p) - y_now(p)
+
+weight(p+1) = weight(p) + ΔWeight(p)
+ΔWeight(p) = α * y_now(p) * δ(p)
+
+α : 自訂的學習率
+δ : 誤差梯度
+
+δ(p) = ∂(y_now(p))/∂(x_all(p)) * diffDelta(p)
+     = y_now(p) * (1-y_now(p)) * diffDelta(p)
+"""
+
+inX1 = [0, 0, 1, 1, 0, 1, 0, 1]
+inX2 = [0, 1, 0, 1, 1, 1, 0, 0]
+outCorrect = [0, 0, 0, 1, 0, 1, 0, 0]
+
+tmpNeuron = neuron([inX1[0], inX2[0]], outCorrect[0])
+tmpNeuron.nextInput()
+tmpNeuron.timerStart()
+tmpNeuron.timerNow()
+tmpNeuron.timerEnd()

develop/dev_ANNs/neuron/neuron.py

@@ -0,0 +1,4 @@
+#!/usr/local/bin/python3
+# -*-coding:utf-8 -*-
+import sys
+import os

import_main.py

@@ -9,12 +9,19 @@
 # ==================================================
 """
 
-from module_for_all import log_thing
-from learning.module import tf_class
+
+
 import sys
 import os
 from functools import wraps
+print("###")
+print(sys.path)
+print("###")
+print(__file__)
+print("###")
 
+from module_for_all import log_thing
+from learning.module import tf_class
 
 class main_learning(log_thing.Timer):
     """

learning/main_learning.py

@@ -11,12 +11,15 @@
 """
 
 from module_for_all import log_thing
-import sys
-import os
+# import sys
+# import os
+from import_main import *
 
 __author__ = "Asa Ewing"
 __version__ = "1.0.0"
 
+print(sys.path)
+
 
 class mainPiBot(log_thing.Timer):
     """

main.py

@@ -16,6 +16,48 @@
 import time
 import subprocess
 
+import inspect
+import re
+
+
+class Basic(object):
+    def __init__(self):
+        return
+
+    @staticmethod
+    def VarName(var):
+
+        for tmp in inspect.getframeinfo(inspect.currentframe().f_back):
+            print("# # # ", tmp)
+
+        for line in inspect.getframeinfo(inspect.currentframe().f_back)[3]:
+            m = re.search(r"\bVarName\s*\(\s*([A-Za-z_][A-Za-z0-9_]*)\s*\)", line)
+            if m:
+                return m.group(1)
+
+    @staticmethod
+    def VarName2(*args):
+        frame = inspect.currentframe()
+        frame = inspect.getouterframes(frame)[1]
+        string = inspect.getframeinfo(frame[0]).code_context[0].strip()
+        args = string[string.find('(') + 1:-1].split(',')
+
+        names = []
+        for i in args:
+            if i.find('=') != -1:
+                names.append(i.split('=')[1].strip())
+                #names = i.split('=')[1].strip()
+            else:
+                names.append(i)
+                #names = i
+
+        return names
+
+    @staticmethod
+    def foo(**kwargs):
+        for arg_name in kwargs:
+            return kwargs[arg_name], arg_name
+
 
 class DateTime(object):
     """