def __init__(self):
c = Convert()
x_train, y_train, x_test, y_test = c.getDTOneHotData()
self.x_train = np.array(x_train)
self.x_test = np.array(x_test)
self.y_train = pd.get_dummies(np.array(y_train), prefix="y")
self.label_value = y_test #list format of label
self.y_test = pd.get_dummies(np.array(y_test), prefix="y")
'''
To classify images using a recurrent neural network, we consider every image
row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then
handle 28 sequences of 28 steps for every sample.
'''
# Parameters
self.learning_rate = 0.01
self.training_iters = 300
self.batch_size = len(self.x_train) # forest-net must total data
self.display_step = 10
# Network Parameters
self.n_input = 418 # feature number
self.n_steps = 4 # timesteps
self.n_hidden = 128 # hidden layer num of features
self.n_classes = 2 # MNIST total classes (0-9 digits)
self.forget_bias = 1.0 # forget bias value
def __init__(self):
c = Convert()
self.x_train, self.y_train, self.x_test, self.y_test = c.get_libsvm_gbdt_data(
"common")
self.y_input = self.y_test
self.y_train = pd.get_dummies(np.array(self.y_train), prefix="y")
self.y_test = pd.get_dummies(np.array(self.y_test), prefix="y")
def get_data(self, tag):
c = Convert()
if tag == "normal":
(self.x_train, self.y_train, self.x_test,
self.y_test) = c.getDTData()
elif tag == "oneHot":
(self.x_train, self.y_train, self.x_test,
self.y_test) = c.getDTOneHotData()
'''
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell
import numpy as np
import pandas as pd
import sys
sys.path.append("../script")
from convert_input import Convert
from validation import Validation
# Import MNIST data
#from tensorflow.examples.tutorials.mnist import input_data
#mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
c = Convert()
x_train, y_train, x_test, y_test = c.getDTOneHotData()
x_train = np.array(x_train)
x_test = np.array(x_test)
y_train = pd.get_dummies(np.array(y_train))
input_value = y_test
y_test = pd.get_dummies(np.array(y_test))
'''
To classify images using a recurrent neural network, we consider every image
row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then
handle 28 sequences of 28 steps for every sample.
'''
# Parameters
learning_rate = 0.01
training_iters = 300
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell
import pandas as pd
import numpy as np
import sys
sys.path.append("../script")
from convert_input import Convert
from validation import Validation
c = Convert()
x_train, y_train, x_test, y_test = c.get_libsvm_gbdt_data("recent")
#x_train = np.array(x_train)
#x_test = np.array(x_test)
print (type(x_train[0]), x_test[0].shape)
#exit()
def conversion(temp):
result = list()
for value in temp:
result.append(value.todense())
return np.array(result)
x_train = conversion(x_train)
x_test = conversion(x_test)
y_input = y_test
y_train = pd.get_dummies(np.array(y_train),prefix="y")