package edu.fudan.util;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.Map;
import java.util.Map.Entry;
import java.util.TreeMap;
import edu.fudan.ml.types.sv.HashSparseVector;
import gnu.trove.iterator.TIntIntIterator;
import gnu.trove.map.hash.TIntFloatHashMap;
import gnu.trove.map.hash.TIntIntHashMap;
/**
* 实现数组排序、直方图的功能
*
* @author xpqiu
* @version 1.0
* @since FudanNLP 1.5
*/
public class MyArrays {
/**
* 记录之前的label和得分，保留前n个
*
* @param score
* @param pred
* @return 插入位置
*/
public static int addBest(float[] scores, Object[] predList, float score, Object pred) {
int n = scores.length;
int i;
for (i = 0; i < n; i++) {
if (score > scores[i])
break;
}
if (i >= n)
return -1;
for (int k = n - 2; k >= i; k--) {
scores[k + 1] = scores[k];
predList[k + 1] = predList[k];
}
scores[i] = score;
predList[i] = pred;
return i;
}
/**
*
* @param freqmap
* @return
*/
public static TreeMap<Integer, Integer> countFrequency(TIntIntHashMap freqmap) {
TreeMap<Integer, Integer> map = new TreeMap<Integer,Integer>();
TIntIntIterator it = freqmap.iterator();
while(it.hasNext()){
it.advance();
int freq = it.value();
if(map.containsKey(freq)){
map.put(freq, map.get(freq)+1);
}else
map.put(freq, 1);
}
return map;
}
/**
*
* @param count
* @param nbin
* @return 直方图
*/
public static float[][] histogram(float[] count, int nbin) {
float maxCount = Float.NEGATIVE_INFINITY;
float minCount = Float.MAX_VALUE;
for (int i = 0; i < count.length; i++) {
if (maxCount < count[i]) {
maxCount = count[i];
}
if (minCount > count[i]) {
minCount = count[i];
}
}
float[][] hist = new float[2][nbin];
float interv = (maxCount - minCount) / nbin;
for (int i = 0; i < count.length; i++) {
int idx = (int) Math.floor((count[i] - minCount) / interv);
if (idx == nbin)
idx--;
hist[0][idx]++;
}
for (int i = 0; i < nbin; i++) {
hist[1][i] = minCount + i * interv;
}
return hist;
}
/**
* 归一化
*
* @param c
*/
public static void normalize(float[] c) {
float max = Float.MIN_VALUE;
float min = Float.MAX_VALUE;
for (int i = 0; i < c.length; i++) {
if (min > c[i])
min = c[i];
if (max < c[i])
max = c[i];
}
float val = max - min;
if (val == 0)
return;
for (int i = 0; i < c.length; i++) {
c[i] = (c[i] - min) / val;
}
}
/**
* 概率归一化
*
* @param c 数组元素必须大于等于0
*/
public static void normalize2Prop(float[] c) {
float sum = sum(c);
if (sum == 0)
return;
for (int i = 0; i < c.length; i++) {
c[i] = c[i] / sum;
}
}
/**
* 对数组的绝对值由大到小排序，返回调整后元素对于的原始下标
*
* @param c
* 待排序数组
* @return 原始下标
*/
public static int[] sort(float[] c) {
HashMap<Integer, Float> map = new HashMap<Integer, Float>();
for (int i = 0; i < c.length; i++) {
if (c[i] != 0.0) {
map.put(i, Math.abs(c[i]));
}
}
ArrayList<Map.Entry<Integer, Float>> list = new ArrayList<Map.Entry<Integer, Float>>(
map.entrySet());
Collections.sort(list, new Comparator<Map.Entry<Integer, Float>>() {
@Override
public int compare(Entry<Integer, Float> o1,
Entry<Integer, Float> o2) {
if (o2.getValue() > o1.getValue()) {
return 1;
} else if (o1.getValue() > o2.getValue()) {
return -1;
} else {
return 0;
}
}
});
int[] idx = new int[list.size()];
for (int i = 0; i < list.size(); i++) {
idx[i] = list.get(i).getKey();
}
return idx;
}
/**
* 得到总能量值大于thres的元素对应的下标
*
* @param c
* @param thres
* @param r
* true表示返回最大的，false表示返回剩余的
* @return 元素下标
*/
public static int[] getTop(float[] c, float thres, boolean r) {
int[] idx = sort(c);
int i;
float total = 0;
float[] cp = new float[idx.length];
for (i = 0; i < idx.length; i++) {
cp[i] = (float) Math.pow(c[idx[i]], 2);
total += cp[i];
}
float ratio = 0;
for (i = 0; i < idx.length; i++) {
ratio += cp[i] / total;
if (ratio > thres)
break;
}
int[] a;
if (r)
a = Arrays.copyOfRange(idx, 0, i);
else
a = Arrays.copyOfRange(idx, i, idx.length);
return a;
}
/**
* 对部分下标的元素赋值
*
* @param c
* 数组
* @param idx
* 赋值下标
* @param v
* 值
*/
public static void set(float[] c, int[] idx, float v) {
for (int i = 0; i < idx.length; i++) {
c[idx[i]] = v;
}
}
/**
* 移除能量值小于一定阈值的项
* @param c 数组
* @param v 阈值
*/
public static void trim(float[] c, float v) {
int[] idx = getTop(c, v, false);
set(c, idx, 0.0f);
}
/**
* 求和
* @param c
* @return 所有元素的和
*/
public static int sum(int[] c) {
int s = 0;
for (int i = 0; i < c.length; i++) {
if (c[i] != 0)
s+=c[i];
}
return s;
}
/**
* 累加
* @param c
* @return 所有元素的和
*/
public static int[] accumulate(int[] c) {
int[] s = new int[c.length];
s[0] =c[0];
for (int i = 1; i < c.length; i++) {
s[i]+=s[i-1]+c[i];
}
return s;
}
/**
* 计算方差
* @param c
* @return
*/
public static float viarance(float[] c) {
float aver = average(c);
float via = 0.0f;
for(int i=0;i<c.length;i++){
float diff = c[i]-aver;
via+=diff*diff;
}
return via/c.length;
}
/**
* 计算熵
* @param c 概率数组
* @return
*/
public static float entropy(float[] c) {
float e = 0.0f;
for(int i=0;i<c.length;i++){
if(c[i]!=0.0&&c[i]!=1){
e -= c[i]*Math.log(c[i]);
}
}
return e;
}
/**
* 计算熵，先将频率转换为概率
* @param c 频率数组
* @return
*/
public static float entropy(int[] c) {
//total 频率总数
float total = sum(c);
if(total==0f)
return 0f;
float[] prop = new float[c.length];
for(int i=0;i<c.length;i++){
prop[i] = c[i]/(total);
}
return entropy(prop);
}
/**
* 求和
* @param c float
* @return 所有元素的和
*/
public static float average(float[] c) {
float s = sum(c);
return s/c.length;
}
/**
* 求和
* @param c float
* @return 所有元素的和
*/
public static float sum(float[] c) {
float s = 0;
for (int i = 0; i < c.length; i++) {
s+=c[i];
}
return s;
}
/**
* 统计非零个数
*
* @param c
* @return 非零元素数量
*/
public static int countNoneZero(float[] c) {
int count = 0;
for (int i = 0; i < c.length; i++) {
if (c[i] != 0.0)
count++;
}
return count;
}
/**
* 统计非零元素
*
* @param c
* @return 非零元素标记
*/
public static boolean[] getNoneZeroIdx(float[] c) {
boolean[] b = new boolean[c.length];
for (int i = 0; i < c.length; i++) {
if (c[i] != 0.0)
b[i] = true;
}
return b;
}
public static int[] string2int(String[] c) {
int[] d = new int[c.length];
for (int i = 0; i < c.length; i++) {
d[i] = Integer.parseInt(c[i]);
}
return d;
}
public static String[] int2string(int[] c) {
String[] d = new String[c.length];
for (int i = 0; i < c.length; i++) {
d[i] = String.valueOf(c[i]);
}
return d;
}
/**
* 对数组的绝对值由大到小排序，返回调整后元素对于的原始下标
*
* @param data
* 待排序数组
* @return 原始下标
*/
public static int[] sort(TIntFloatHashMap data) {
return MyCollection.sort(data);
}
/**
* 得到总能量值大于thres的元素对应的下标
*
* @param data 稀疏向量
* @param thres
* @return 元素下标 int[][] 第一列表示大于阈值的元素 第二列表示小于阈值的元素
*/
public static int[][] getTop(TIntFloatHashMap data, float thres) {
int[] idx = sort(data);
int i;
float total = 0;
float[] cp = new float[idx.length];
for (i = idx.length; i-- > 0;) {
cp[i] = (float) Math.pow(data.get(idx[i]), 2);
total += cp[i];
}
float ratio = 0;
for (i = 0; i < idx.length; i++) {
ratio += cp[i] / total;
if (ratio > thres)
break;
}
int[][] a = new int[2][];
a[0] = Arrays.copyOfRange(idx, 0, i);
a[1] = Arrays.copyOfRange(idx, i, idx.length);
return a;
}
/**
* 对部分下标的元素赋零
*
* @param data
* 数组
* @param idx
* 赋值下标
*/
public static void setZero(TIntFloatHashMap data, int[] idx) {
for(int i = 0; i < idx.length; i++) {
if (data.containsKey(idx[i])) {
data.remove(idx[i]);
}
}
}
/**
* 移除能量值小于一定阈值的项
* @return
*
*/
public static int[] trim(TIntFloatHashMap data, float v) {
int[][] idx = getTop(data, v);
setZero(data, idx[1]);
return idx[0];
}
/**
* 移除能量值小于一定阈值的项
*
*/
public static void trim(HashSparseVector c, float v) {
trim(c.data,v);
}
}