Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)
That's it!
Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)
That's it!
from sys import argv | |
from base64 import b64encode | |
from datetime import datetime | |
from Crypto.Hash import SHA, HMAC | |
def create_signature(secret_key, string): | |
""" Create the signed message from api_key and string_to_sign """ | |
string_to_sign = string.encode('utf-8') | |
hmac = HMAC.new(secret_key, string_to_sign, SHA) | |
return b64encode(hmac.hexdigest()) |
import pandas as pd | |
from random import random | |
flow = (list(range(1,10,1)) + list(range(10,1,-1)))*100 | |
pdata = pd.DataFrame({"a":flow, "b":flow}) | |
pdata.b = pdata.b.shift(9) | |
data = pdata.iloc[10:] * random() # some noise | |
import numpy as np |
#!/usr/bin/env python | |
""" | |
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction. | |
""" | |
from __future__ import print_function, division | |
import numpy as np | |
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten | |
from keras.models import Sequential |
import tensorflow as tf | |
import numpy as np | |
FC_SIZE = 1024 | |
DTYPE = tf.float32 | |
def _weight_variable(name, shape): | |
return tf.get_variable(name, shape, DTYPE, tf.truncated_normal_initializer(stddev=0.1)) |
#!/usr/bin/python3 | |
from cnn import cnn | |
import hyperopt | |
def objective(args): | |
params = cnn.ExperimentParameters() |