jonpappalord / ditras Goto Github PK

I did clone of the project. When I execute the "python DITRAS.py 10000 720 location2info_trentino od_matrix.pkl diary_generator_1hour.pkl trajs_10000_720.csv " command, I receive the error message:

ValueError: could not convert string to float

Traceback Full:

Traceback (most recent call last):
  File "DITRAS.py", line 467, in <module>
    class DITRAS(object):
  File "DITRAS.py", line 474, in DITRAS
    def __init__(self, n_agents=10000, length=168, diary_generator=MD(),
  File "DITRAS.py", line 396, in __init__
    self.markov_model = pickle.load(open(filename, 'rb'))
ValueError: could not convert string to float

OS: Windows 10
Python 3.7.5

Thanks a lot for your work. Can this library simulate the interactions between multiple agents?

The documentation mentions we can use a diary_generator_builder.py to create our own diary generator, however such a file is not included.

Would it be possible for you to git push that file to the repo, please 🙏?
Thank you so much

Training.py

import numpy
import tensorflow as tf
from tqdm import tqdm
import os
from pandas import np

from Data import trainDataSet, testDataSet
from MultiLayerModel import y, y_, x

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=y + 1e-50, labels=y_))

-GradientDescentOptimizer is used to minimize loss
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

-Define accuracy
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

-Train
epochs = 50000
train_acc = numpy.zeros(epochs // 10)
test_acc = numpy.zeros(epochs // 10)

for i in tqdm(range(epochs), ascii=True):
if i % 10 == 0: # Record summary data, and the accuracy
- Check accuracy on train set
A = accuracy.eval(feed_dict={x: trainDataSet.drop(['font', 'fontVariant'], axis=1), y_: trainDataSet['font']})
train_acc[i // 10] = A
- And now the validation set
A = accuracy.eval(feed_dict={x: testDataSet.drop(['font', 'fontVariant'], axis=1), y_: testDataSet['font']})

    test_acc[i // 10] = A

train_step.run(feed_dict={x: trainDataSet.drop(['font', 'fontVariant'], axis=1), y_: trainDataSet[['font']]})

Multilayermodel.py
import math

import tensorflow as tf

• Create a Tensorflow session
  session = tf.InteractiveSession()

• These will be inputs for the model

• Input pixels of images, flattened

• 400 = 20*20 which is the size
  x = tf.placeholder("float", [None, 410])

• Known labels
  y_ = tf.placeholder("float", [None, 2])

• Hidden layer 1 with 256 neurons
  num_hidden1 = 256

• Variables

• W1 is for weights

• b1 is for bias
  W1 = tf.Variable(tf.truncated_normal([410, num_hidden1], stddev=1. / math.sqrt(410)))
  b1 = tf.Variable(tf.constant(0.1, shape=[num_hidden1]))

• Compute the activation function of the weighted sum -> produce 128 intermediate value

• Nonlinear transform functions - activation function: sigmoid
  h1 = tf.sigmoid(tf.matmul(x, W1) + b1)

• Hidden Layer 2 with 64 neurons
  num_hidden2 = 64
  W2 = tf.Variable(tf.truncated_normal([num_hidden1, num_hidden2], stddev=2. / math.sqrt(num_hidden1)))
  b2 = tf.Variable(tf.constant(0.2, shape=[num_hidden2]))
  h2 = tf.sigmoid(tf.matmul(h1, W2) + b2)

• Output Layer

• Logistic regression again
  W3 = tf.Variable(tf.truncated_normal([num_hidden2, 2], stddev=1. / math.sqrt(2)))
  b3 = tf.Variable(tf.constant(0.1, shape=[2]))

• Just initialize
  session.run(tf.global_variables_initializer())

• Define model
  y = tf.nn.softmax(tf.matmul(h2, W3) + b3)