EastonLee

collective_code_location_message = '\n'
def print_dict(dict, verbose=True):
    #callerframerecord = inspect.stack()[1]    # 0 represents this line
    #                                        # 1 represents line at caller
    #frame = callerframerecord[0]
    #info = inspect.getframeinfo(frame)
    #if info.function == 'print_sql_result':
    #    code_probe('',2)
    #else:
    #    code_probe('', 1)

EastonLee

#!/usr/bin/env python
from gevent import monkey 
monkey.patch_all()
import sys
import redis
import urllib2
import traceback
from cgi import escape

# -- gsdproxy 

EastonLee

function [J grad] = nnCostFunction(nn_params, ...
                                   input_layer_size, ...
                                   hidden_layer_size, ...
                                   num_labels, ...
                                   X, y, lambda)
%NNCOSTFUNCTION Implements the neural network cost function for a two layer
%neural network which performs classification
%   [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
%   X, y, lambda) computes the cost and gradient of the neural network. The
%   parameters for the neural network are "unrolled" into the vector

EastonLee

---
layout: null
sitemap:
  exclude: 'yes'
---
<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
  {% for post in site.posts %}
    {% unless post.published == false %}
    <url>

EastonLee

#!/usr/bin/env python
# coding=utf-8

'''
This is not a difficult problem to solve, but the highlight of this problem-solving process is that
first I analyzed the vague problem very carefully and considered many edge cases, and
I solved it in 3 different ways from a naive way to a high-performance DP way and
I tested it with extensive test cases far beyond the scale of the problem asked to solve and
I gave the time and space complexity.

EastonLee

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