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# regex pattern: [\w.%+-]+@[\w.-]+\.[a-zA-Z]{2,6}

# equivalent rxe code:
username = rxe.one_or_more(rxe.set([rxe.alphanumeric(), '.', '%', '+', '-']))
domain = (rxe
	.one_or_more(rxe.set([rxe.alphanumeric(), '.', '-']))
	.literal('.')
	.at_least_at_most(2, 6, rxe.set([rxe.range('a', 'z'), rxe.range('A', 'Z')]))
)
email = (rxe

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torch.set_default_tensor_type('torch.cuda.FloatTensor')

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import torch
dim = 2
A = torch.rand(dim, dim, requires_grad=False)
b = torch.rand(dim, 1,  requires_grad=False)
x = torch.autograd.Variable(torch.rand(dim, 1), requires_grad=True)
stop_loss = 1e-2
step_size = stop_loss / 3.0
print('Loss before: %s' % (torch.norm(torch.matmul(A, x) - b)))
for i in range(1000*1000):
    Δ = torch.matmul(A, x) - b

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stop_loss = 1e-2
step_size = stop_loss / 3.0

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x.data -= step_size * x.grad.data

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L.backward()

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Δ = torch.matmul(A, x) - b
L = torch.norm(Δ, p=2)

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import torch
dim = 2
A = torch.rand(dim, dim, requires_grad=False)
b = torch.rand(dim, 1,   requires_grad=False)
x = torch.autograd.Variable(torch.rand(dim, 1), requires_grad=True)

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from airflow import DAG
from lib.dag_presto_ds_insert import dag_presto_ds_insert
from airflow.operators import PrestoOperator, DsPartitionSensor
from datetime import date, datetime, timedelta

def dag_growth_accounting(
        base_table_name,
        start_date,
        events_table,
        days=1,

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F1 metrics http://f1metrics.wordpress.com/

Applied Predictive Modeling book http://www.amazon.com/Applied-Predictive-Modeling-Max-Kuhn/dp/1461468485/ref=pd_sim_b_1?ie=UTF8&refRID=0XDCXS3G3RMVDZABVV90

Technical Analysis http://zerodha.com/varsity/module/technical-analysis/