Exemplo n.º 1
0
import time
import random
from bandit import Bandit

for i in range(250):
    print i
    time.sleep(0.01)

with open('output-files/stuff.txt', 'wb') as f:
    f.write("HI!")

bandit = Bandit()
bandit.metadata.x = 1
bandit.metadata.y2 = 0.83
bandit.metadata.r2 = "hello!"

for x in range(10):
    for y in range(10):
        for tag in ["a", "b", "c", "d", "e", "f", "g"]:
            bandit.report(tag, y, random.normalvariate(0, 1))
        time.sleep(0.1)

# email = job.Email("*****@*****.**", "This is a test email", "Hello self!")
# email._write()
Exemplo n.º 2
0
import pandas as pd
from bandit import Bandit
import time

bandit = Bandit()
df = pd.read_csv("./report-data.csv")

for _, row in df.iterrows():
    row = row.to_dict()
    bandit.report(row['tag_name'], row['y'])
    time.sleep(0.1)
Exemplo n.º 3
0
# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.neg(xte))), reduction_indices=1)
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.arg_min(distance, 0)

accuracy = 0.

# Initializing the variables
init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # loop over test data
    for i in range(len(Xte)):
        # Get nearest neighbor
        nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})
        # Get nearest neighbor class label and compare it to its true label
        print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \
            "True Class:", np.argmax(Yte[i]))
        # Calculate accuracy
        if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
            accuracy += 1. / len(Xte)
            print('accuracy:', accuracy)
            bandit.report('Accuracty', accuracy)

    print("Done!")
    print("Accuracy:", accuracy)
Exemplo n.º 4
0
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost],
                            feed_dict={
                                x: batch_xs,
                                y: batch_ys
                            })
            # Compute average loss
            avg_cost += c / total_batch
        # Display logs per epoch step
        if (epoch + 1) % display_step == 0:
            print("Epoch:", '%04d' % (epoch + 1), "cost=",
                  "{:.9f}".format(avg_cost))
            bandit.report('Cost', float(avg_cost))

    print("Optimization Finished!")

    # Test model
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # Calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:",
          accuracy.eval({
              x: mnist.test.images,
              y: mnist.test.labels
          }))
    bandit.metadata.accuracy = float(
        accuracy.eval({
            x: mnist.test.images,