irwenqiang

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.

irwenqiang

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

irwenqiang

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 {

irwenqiang

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

irwenqiang

#!/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):

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