alexbw

import numpy as np
import cPickle as pickle
import joblib
from moseq.train import ARHMM, train_model
from moseq.train.util import whiten_all
from collections import OrderedDict
from syllables import analysis

# Load the data
with open("/data/efs/drugs/alldoses/dataset.pkl","r") as f:

alexbw

def f(x):
  assert x != 0, 'Do not pass zero!'
  return x * x

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def f(n):
  z = []
  # We ask you to tell us the element dtype of the list
  autograph.set_element_type(z, tf.int32)
  for i in range(n):
    z.append(i)
  # when you're done with the list, stack it
  # (this is just like np.stack)
  return autograph.stack(z) 

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def f(n):
  if n >= 0:
    while n < 5:
      n += 1
      print(n)
  return n

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def collatz(a):
    counter = 0
    while a != 1:
        if a % 2 == 0:
            a = a // 2
        else:
            a = 3 * a + 1
        counter = counter + 1
    return counter

alexbw

with tf.Graph().as_default():  
  x_tensor = tf.constant(9.0)

  # The converted function works like a regular op: tensors in, tensors out.
  huber_loss_tensor = huber_loss(x_tensor)

  with tf.Session() as sess:
    print('TensorFlow result: %2.2f\n' % sess.run(huber_loss_tensor))

alexbw

def tf__huber_loss(a):
  with tf.name_scope('huber_loss'):

    def if_true():
      with tf.name_scope('if_true'):
        loss = a * a / 2
        return loss,

    def if_false():
      with tf.name_scope('if_false'):

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@autograph.convert()
def huber_loss(a):
  if tf.abs(a) <= delta:
    loss = a * a / 2
  else:
    loss = delta * (tf.abs(a) - delta / 2)
  return loss

alexbw

def huber_loss(a):
  if tf.abs(a) <= delta:
    loss = a * a / 2
  else:
    loss = delta * (tf.abs(a) - delta / 2)
  return loss

alexbw

config defaultToCurrentScreen false
# Shows app icons and background apps, spreads icons in the same place.
config windowHintsShowIcons true
config windowHintsIgnoreHiddenWindows false
config windowHintsSpread true

config nudgePercentOf screenSize
config resizePercentOf screenSize

bind e:cmd;alt hint QWERASDFZXCV # use whatever keys you want