Python

python/src/ai/network.py

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+import json
+import os
+import tensorflow as tf
+import sklearn
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+
+import src.contabilidad.pdf as pdf
+import src.contabilidad.text_handler as th
+
+
+class Layer:
+    def __init__(self):
+        self.__weights = None
+        self.__bias = None
+
+    def set_size(self, inputs: int, size: int):
+        self.__weights = [[0 for j in range(0, inputs)] for i in range(0, size)]
+        self.__bias = [0 for i in range(0, size)]
+
+    def add_weight(self, vector: list, idx: int = None):
+        if idx is None:
+            self.__weights.append(vector)
+            return self
+        self.__weights = self.__weights[:idx] + [vector] + self.__weights[idx:]
+        return self
+
+    def set_weight(self, value: float, weight_index: int, input_index: int):
+        self.__weights[weight_index][input_index] = value
+
+    def set_bias(self, value: list):
+        self.__bias = value
+
+    def train(self, input_values: list, output_values: list):
+        output = self.get_output(input_values)
+        errors = []
+        for i, v in enumerate(output):
+            error = (output_values[i] - v) / output_values[i]
+            new_value = v * error
+
+    def to_json(self):
+        return {
+            'bias': self.__bias,
+            'weights': self.__weights
+        }
+
+    def get_output(self, vector: list):
+        output = []
+        for i, weight in enumerate(self.__weights):
+            val = 0
+            for j, v in enumerate(weight):
+                val += v * vector[j]
+            output[i] = val + self.__bias[i]
+        return output
+
+
+def layer_factory(layer_dict: dict):
+    layer = Layer()
+    layer.set_bias(layer_dict['bias'])
+    [layer.add_weight(w) for w in layer_dict['weights']]
+    return layer
+
+
+class Network:
+    def __init__(self, filename: str):
+        self._filename = filename
+        self.__layers = None
+
+    def load(self):
+        with open(self._filename) as f:
+            data = json.load(f)
+            if 'layers' in data.keys():
+                self.add_layers(data['layers'])
+
+    def add_layers(self, layers: list):
+        for lr in layers:
+            layer = layer_factory(lr)
+            self.__layers.append(layer)
+
+
+class AI:
+    def __init__(self, dictionary_filename, logger):
+        self.__dict = None
+        self.__network = None
+        self.__sources = None
+        self.filename = ''
+
+    def add_source(self, text):
+        if self.__sources is None:
+            self.__sources = []
+        self.__sources.append(text)
+        return self
+
+    def set_filename(self, filename: str):
+        self.filename = filename
+        return self
+
+    def process_sources(self):
+        for source in self.__sources:
+            self.process(**source)
+
+    def process(self, filename, password):
+        encoder = LabelEncoder()
+        t = filename.split('.')
+        temp = os.path.realpath(os.path.join(os.path.dirname(filename), t[0] + '-temp.pdf'))
+        pdf.remove_encryption(filename, password, temp)
+        obj = pdf.get_text(temp)
+        os.remove(temp)
+        word_list = th.split_words(obj)
+        fits = encoder.fit_transform(word_list)
+        print(fits)
+        phrases = []
+        for length in range(1, len(word_list) + 1):
+            for start in range(0, len(word_list)):
+                phrase = word_list[start:(start + length)]
+                phrase = np.append(np.array([fits[word_list.index(w)] for w in phrase]),
+                                   np.zeros([len(word_list) - len(phrase)]))
+                phrases.append(phrase)
+        phrases = np.array(phrases)
+        print(phrases.shape)
+
+    def active_train(self):
+        pass