irwenqiang

• Next Permutation

  STL风格版本，利用reverse_iterator简化边界判断，可处理重复元素。参见：生成全排列、下一个排列、第k个排列的。

• Unique Binary Search Trees

  一维DP版本，时间复杂度O(N^2)，空间复杂度O(N)

• Recover Binary Search Tree

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#!/usr/bin/env python

from numpy import asmatrix, asarray, ones, zeros, mean, sum, arange, prod, dot, loadtxt
from numpy.random import random, randint
import pickle

MISSING_VALUE = -1 # a constant I will use to denote missing integer values

def impute_hidden_node(E, I, theta, sample_hidden):

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/**
 * iver:
 * In ALS, we factorize the matrix into two lower rank matrix such that A ~= U*V'
 * It is not a symmetric matrix factorization where A ~= Q' Q as you write.
 * 
 * The non zero entries of the matrix A are the edges between user and item nodes. 
 * The edge direction is always between user -> item.
 * 
 * The user latent vectors are stored in the user vertices (vertex.num_out_edges() > 0).
 * The item latent vectors are stored in the item vertices (vertex.num_out_edges() == 0)

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/**  
 * Copyright (c) 2009 Carnegie Mellon University. 
 *     All rights reserved.
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *

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package topic

import spark.broadcast._
import spark.SparkContext
import spark.SparkContext._
import spark.RDD
import spark.storage.StorageLevel
import scala.util.Random
import scala.math.{ sqrt, log, pow, abs, exp, min, max }
import scala.collection.mutable.HashMap

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import spark.SparkContext
import SparkContext._

/**
 * A port of [[http://blog.echen.me/2012/02/09/movie-recommendations-and-more-via-mapreduce-and-scalding/]]
 * to Spark.
 * Uses movie ratings data from MovieLens 100k dataset found at [[http://www.grouplens.org/node/73]]
 */
object MovieSimilarities {

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package topic

import spark.broadcast._
import spark.SparkContext
import spark.SparkContext._
import spark.RDD
import spark.storage.StorageLevel
import scala.util.Random
import scala.math.{ sqrt, log, pow, abs, exp, min, max }
import scala.collection.mutable.HashMap

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import numpy as np
from scipy import linalg, optimize

MAX_ITER = 100


def group_lasso(X, y, alpha, groups, max_iter=MAX_ITER, rtol=1e-6,
             verbose=False):
    """
    Linear least-squares with l2/l1 regularization solver.

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#include <algorithm>
#include <map>
#include <set>
#include <string>
#include <utility>
#include <vector>

#include "hdf5.h"

#include "caffe/common.hpp"

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#ifndef CAFFE_NET_HPP_
#define CAFFE_NET_HPP_

#include <map>
#include <set>
#include <string>
#include <utility>
#include <vector>

#include "caffe/blob.hpp"