抽奖砍价之类以均值为基础的波动算法 demo版本

<?php/*
砍价小程序生成 以平均值为波动的随机砍价算法 此算法有点low
但是可以实现基本上 以平均值为波动的 目前只能满足 平均值小于10和小于100的算法实现
如果基数小那么可以有更好的算法 因为公司要求500元得价格 大约300-400人才能获得。。。所以如果波动很大 那么会有更多的重复值
只做一个参考 demo 用吧
最后求得数组 总价格会有偏差 但是基本上多出0.1来也可以忽略不计

代码里得注释可以忽略 基本上没啥用 有的也没删除。。。
*/

<?phpfunction suanfa($money,$people,&$arr,&$nowmoney){$sheng_money = 0;$sheng_people = 0;$num=0;//先求平均值//$pinjun = round($money/$people,2);// echo $pinjun;exit;$i=0;$j=0;if($pinjun<10){$pinjun_num = $pinjun*100;$jia = 0.01;$jian=0.01;if($people>($pinjun_num*2)){for($i=0;$i<$pinjun_num;$i++){$arr[$i] = $pinjun+$jia;$jia=$jia+0.01;}for($i=0;$i<($pinjun_num-1);$i++){$arr[$i+$pinjun_num]=round(($pinjun-$jian)*100,2)/100;$jian=$jian+0.01;}//如果大于他 会有剩下的值 $count_num = count($arr);$sheng_num = $people-$count_num;//echo $sheng_num;//151 之前是249; //或许不用//求一下剩余的总价$nowmoney = array_sum($arr);$sheng_money = $money-$nowmoney;for($i=$count_num;$i<$people;$i++){//最后一个if($i==$people-1){$arr[$i] = round($money-array_sum($arr),2);if($arr[$i]<0.01){$arr[$i] = 0.01;}}else{if($i==$count_num){$arr[$i] =  round(round(($sheng_money/$sheng_num*1.5)*100,2)/100,2);if($arr[$i]<0.01){$arr[$i] = 0.01;}$hai_shengxia_money = $sheng_money-$arr[$i];$sheng_num--;}else{$arr[$i] =  round(round(($hai_shengxia_money/$sheng_num*1.5)*100,2)/100,2);if($arr[$i]<0.01){$arr[$i] = 0.01;}$hai_shengxia_money = $hai_shengxia_money-$arr[$i];$sheng_num--;}}}if(array_sum($arr)<$money){$arr[0] += round(($money-array_sum($arr) )*100,2)/100; }}else{//$pingjun=2.5;for($i=0;$i<($people/2);$i++){$arr[$i] = $pinjun+$jia;$jia=$jia+0.01;}for($i=0;$i<($people/2);$i++){$arr[$i+$people/2]=round(($pinjun-$jian)*100,2)/100;$jian=$jian+0.01;}if(array_sum($arr)<$money){$arr[0] += round(($money-array_sum($arr) )*100,2)/100; }}}elseif($pinjun<100){$pinjun_num = $pinjun*100;$jia = 0.1;$jian=0.1;if($people>($pinjun_num*2)){for($i=0;$i<$pinjun_num;$i++){$arr[$i] = $pinjun+$jia;$jia=$jia+0.1;}for($i=0;$i<($pinjun_num-1);$i++){$arr[$i+$pinjun_num]=round(($pinjun-$jian)*100,2)/100;$jian=$jian+0.1;}//如果大于他 会有剩下的值 $count_num = count($arr);$sheng_num = $people-$count_num;//echo $sheng_num;//151 之前是249; //或许不用//求一下剩余的总价$nowmoney = array_sum($arr);$sheng_money = $money-$nowmoney;for($i=$count_num;$i<$people;$i++){//最后一个if($i==$people-1){$arr[$i] = round($money-array_sum($arr),2);if($arr[$i]<0.1){$arr[$i] = 0.1;}}else{if($i==$count_num){$arr[$i] =  round(round(($sheng_money/$sheng_num*1.5)*100,2)/100,2);if($arr[$i]<0.1){$arr[$i] = 0.1;}$hai_shengxia_money = $sheng_money-$arr[$i];$sheng_num--;}else{$arr[$i] =  round(round(($hai_shengxia_money/$sheng_num*1.5)*100,2)/100,2);if($arr[$i]<0.1){$arr[$i] = 0.1;}$hai_shengxia_money = $hai_shengxia_money-$arr[$i];$sheng_num--;}}}if(array_sum($arr)<$money){$arr[0] += round(($money-array_sum($arr) )*100,2)/100; }}else{//$pingjun=2.5;for($i=0;$i<($people/2);$i++){$arr[$i] = round(($pinjun+$jian)*100,2)/100;//$pinjun+$jia;$jia=$jia+0.1;}for($i=0;$i<($people/2);$i++){$arr[$i+$people/2]=round(($pinjun-$jian)*100,2)/100;$jian=$jian+0.1;}if(array_sum($arr)<$money){$arr[0] += round(($money-array_sum($arr) )*100,2)/100; }}}
}$money = 199;
$people = 5;
$arr = array();
$nowmoney = 0;
suanfa($money,$people,$arr,$nowmoney);echo array_sum($arr);shuffle($arr);//print_r($arr);exit;echo(json_encode($arr));exit;/*
ALTER TABLE `hhs_activity_data`
ADD COLUMN `probability`  text NOT NULL AFTER `ext3`;还得改表的字段 不能用int总价格500  人数30
数组最后的总价500.1
Array
([0] => 16.77[1] => 16.87[2] => 16.97[3] => 17.07[4] => 17.17[5] => 17.27[6] => 17.37[7] => 17.47[8] => 17.57[9] => 17.67[10] => 17.77[11] => 17.87[12] => 17.97[13] => 18.07[14] => 18.17[15] => 16.57[16] => 16.47[17] => 16.37[18] => 16.27[19] => 16.17[20] => 16.07[21] => 15.97[22] => 15.87[23] => 15.77[24] => 15.67[25] => 15.57[26] => 15.47[27] => 15.37[28] => 15.27[29] => 15.17
)这个是总价格500 人数400
最后求得的数组总价500
Array
([0] => 1.26[1] => 1.27[2] => 1.28[3] => 1.29[4] => 1.3[5] => 1.31[6] => 1.32[7] => 1.33[8] => 1.34[9] => 1.35[10] => 1.36[11] => 1.37[12] => 1.38[13] => 1.39[14] => 1.4[15] => 1.41[16] => 1.42[17] => 1.43[18] => 1.44[19] => 1.45[20] => 1.46[21] => 1.47[22] => 1.48[23] => 1.49[24] => 1.5[25] => 1.51[26] => 1.52[27] => 1.53[28] => 1.54[29] => 1.55[30] => 1.56[31] => 1.57[32] => 1.58[33] => 1.59[34] => 1.6[35] => 1.61[36] => 1.62[37] => 1.63[38] => 1.64[39] => 1.65[40] => 1.66[41] => 1.67[42] => 1.68[43] => 1.69[44] => 1.7[45] => 1.71[46] => 1.72[47] => 1.73[48] => 1.74[49] => 1.75[50] => 1.76[51] => 1.77[52] => 1.78[53] => 1.79[54] => 1.8[55] => 1.81[56] => 1.82[57] => 1.83[58] => 1.84[59] => 1.85[60] => 1.86[61] => 1.87[62] => 1.88[63] => 1.89[64] => 1.9[65] => 1.91[66] => 1.92[67] => 1.93[68] => 1.94[69] => 1.95[70] => 1.96[71] => 1.97[72] => 1.98[73] => 1.99[74] => 2[75] => 2.01[76] => 2.02[77] => 2.03[78] => 2.04[79] => 2.05[80] => 2.06[81] => 2.07[82] => 2.08[83] => 2.09[84] => 2.1[85] => 2.11[86] => 2.12[87] => 2.13[88] => 2.14[89] => 2.15[90] => 2.16[91] => 2.17[92] => 2.18[93] => 2.19[94] => 2.2[95] => 2.21[96] => 2.22[97] => 2.23[98] => 2.24[99] => 2.25[100] => 2.26[101] => 2.27[102] => 2.28[103] => 2.29[104] => 2.3[105] => 2.31[106] => 2.32[107] => 2.33[108] => 2.34[109] => 2.35[110] => 2.36[111] => 2.37[112] => 2.38[113] => 2.39[114] => 2.4[115] => 2.41[116] => 2.42[117] => 2.43[118] => 2.44[119] => 2.45[120] => 2.46[121] => 2.47[122] => 2.48[123] => 2.49[124] => 2.5[125] => 1.24[126] => 1.23[127] => 1.22[128] => 1.21[129] => 1.2[130] => 1.19[131] => 1.18[132] => 1.17[133] => 1.16[134] => 1.15[135] => 1.14[136] => 1.13[137] => 1.12[138] => 1.11[139] => 1.1[140] => 1.09[141] => 1.08[142] => 1.07[143] => 1.06[144] => 1.05[145] => 1.04[146] => 1.03[147] => 1.02[148] => 1.01[149] => 1[150] => 0.99[151] => 0.98[152] => 0.97[153] => 0.96[154] => 0.95[155] => 0.94[156] => 0.93[157] => 0.92[158] => 0.91[159] => 0.9[160] => 0.89[161] => 0.88[162] => 0.87[163] => 0.86[164] => 0.85[165] => 0.84[166] => 0.83[167] => 0.82[168] => 0.81[169] => 0.8[170] => 0.79[171] => 0.78[172] => 0.77[173] => 0.76[174] => 0.75[175] => 0.74[176] => 0.73[177] => 0.72[178] => 0.71[179] => 0.7[180] => 0.69[181] => 0.68[182] => 0.67[183] => 0.66[184] => 0.65[185] => 0.64[186] => 0.63[187] => 0.62[188] => 0.61[189] => 0.6[190] => 0.59[191] => 0.58[192] => 0.57[193] => 0.56[194] => 0.55[195] => 0.54[196] => 0.53[197] => 0.52[198] => 0.51[199] => 0.5[200] => 0.49[201] => 0.48[202] => 0.47[203] => 0.46[204] => 0.45[205] => 0.44[206] => 0.43[207] => 0.42[208] => 0.41[209] => 0.4[210] => 0.39[211] => 0.38[212] => 0.37[213] => 0.36[214] => 0.35[215] => 0.34[216] => 0.33[217] => 0.32[218] => 0.31[219] => 0.3[220] => 0.29[221] => 0.28[222] => 0.27[223] => 0.26[224] => 0.25[225] => 0.24[226] => 0.23[227] => 0.22[228] => 0.21[229] => 0.2[230] => 0.19[231] => 0.18[232] => 0.17[233] => 0.16[234] => 0.15[235] => 0.14[236] => 0.13[237] => 0.12[238] => 0.11[239] => 0.1[240] => 0.09[241] => 0.08[242] => 0.07[243] => 0.06[244] => 0.05[245] => 0.04[246] => 0.03[247] => 0.02[248] => 0.01[249] => 1.86[250] => 1.86[251] => 1.85[252] => 1.84[253] => 1.84[254] => 1.83[255] => 1.83[256] => 1.82[257] => 1.81[258] => 1.81[259] => 1.8[260] => 1.79[261] => 1.79[262] => 1.78[263] => 1.77[264] => 1.77[265] => 1.76[266] => 1.75[267] => 1.75[268] => 1.74[269] => 1.73[270] => 1.73[271] => 1.72[272] => 1.71[273] => 1.71[274] => 1.7[275] => 1.69[276] => 1.69[277] => 1.68[278] => 1.67[279] => 1.67[280] => 1.66[281] => 1.65[282] => 1.65[283] => 1.64[284] => 1.63[285] => 1.62[286] => 1.62[287] => 1.61[288] => 1.6[289] => 1.6[290] => 1.59[291] => 1.58[292] => 1.57[293] => 1.57[294] => 1.56[295] => 1.55[296] => 1.54[297] => 1.54[298] => 1.53[299] => 1.52[300] => 1.51[301] => 1.51[302] => 1.5[303] => 1.49[304] => 1.48[305] => 1.48[306] => 1.47[307] => 1.46[308] => 1.45[309] => 1.44[310] => 1.44[311] => 1.43[312] => 1.42[313] => 1.41[314] => 1.4[315] => 1.39[316] => 1.39[317] => 1.38[318] => 1.37[319] => 1.36[320] => 1.35[321] => 1.34[322] => 1.34[323] => 1.33[324] => 1.32[325] => 1.31[326] => 1.3[327] => 1.29[328] => 1.28[329] => 1.27[330] => 1.26[331] => 1.26[332] => 1.25[333] => 1.24[334] => 1.23[335] => 1.22[336] => 1.21[337] => 1.2[338] => 1.19[339] => 1.18[340] => 1.17[341] => 1.16[342] => 1.15[343] => 1.14[344] => 1.13[345] => 1.12[346] => 1.11[347] => 1.1[348] => 1.09[349] => 1.08[350] => 1.07[351] => 1.05[352] => 1.04[353] => 1.03[354] => 1.02[355] => 1.01[356] => 1[357] => 0.99[358] => 0.98[359] => 0.96[360] => 0.95[361] => 0.94[362] => 0.93[363] => 0.91[364] => 0.9[365] => 0.89[366] => 0.88[367] => 0.86[368] => 0.85[369] => 0.84[370] => 0.82[371] => 0.81[372] => 0.79[373] => 0.78[374] => 0.76[375] => 0.75[376] => 0.73[377] => 0.72[378] => 0.7[379] => 0.68[380] => 0.67[381] => 0.65[382] => 0.63[383] => 0.61[384] => 0.59[385] => 0.57[386] => 0.55[387] => 0.53[388] => 0.51[389] => 0.49[390] => 0.46[391] => 0.44[392] => 0.41[393] => 0.38[394] => 0.35[395] => 0.31[396] => 0.27[397] => 0.23[398] => 0.17[399] => 0.06
)价格500 人数1000
数组总价格500
Array
([0] => 0.51[1] => 0.52[2] => 0.53[3] => 0.54[4] => 0.55[5] => 0.56[6] => 0.57[7] => 0.58[8] => 0.59[9] => 0.6[10] => 0.61[11] => 0.62[12] => 0.63[13] => 0.64[14] => 0.65[15] => 0.66[16] => 0.67[17] => 0.68[18] => 0.69[19] => 0.7[20] => 0.71[21] => 0.72[22] => 0.73[23] => 0.74[24] => 0.75[25] => 0.76[26] => 0.77[27] => 0.78[28] => 0.79[29] => 0.8[30] => 0.81[31] => 0.82[32] => 0.83[33] => 0.84[34] => 0.85[35] => 0.86[36] => 0.87[37] => 0.88[38] => 0.89[39] => 0.9[40] => 0.91[41] => 0.92[42] => 0.93[43] => 0.94[44] => 0.95[45] => 0.96[46] => 0.97[47] => 0.98[48] => 0.99[49] => 1[50] => 0.49[51] => 0.48[52] => 0.47[53] => 0.46[54] => 0.45[55] => 0.44[56] => 0.43[57] => 0.42[58] => 0.41[59] => 0.4[60] => 0.39[61] => 0.38[62] => 0.37[63] => 0.36[64] => 0.35[65] => 0.34[66] => 0.33[67] => 0.32[68] => 0.31[69] => 0.3[70] => 0.29[71] => 0.28[72] => 0.27[73] => 0.26[74] => 0.25[75] => 0.24[76] => 0.23[77] => 0.22[78] => 0.21[79] => 0.2[80] => 0.19[81] => 0.18[82] => 0.17[83] => 0.16[84] => 0.15[85] => 0.14[86] => 0.13[87] => 0.12[88] => 0.11[89] => 0.1[90] => 0.09[91] => 0.08[92] => 0.07[93] => 0.06[94] => 0.05[95] => 0.04[96] => 0.03[97] => 0.02[98] => 0.01[99] => 0.75[100] => 0.75[101] => 0.75[102] => 0.75[103] => 0.75[104] => 0.75[105] => 0.75[106] => 0.75[107] => 0.75[108] => 0.75[109] => 0.74[110] => 0.74[111] => 0.74[112] => 0.74[113] => 0.74[114] => 0.74[115] => 0.74[116] => 0.74[117] => 0.74[118] => 0.74[119] => 0.74[120] => 0.74[121] => 0.74[122] => 0.74[123] => 0.74[124] => 0.74[125] => 0.74[126] => 0.74[127] => 0.74[128] => 0.74[129] => 0.74[130] => 0.74[131] => 0.74[132] => 0.74[133] => 0.73[134] => 0.73[135] => 0.73[136] => 0.73[137] => 0.73[138] => 0.73[139] => 0.73[140] => 0.73[141] => 0.73[142] => 0.73[143] => 0.73[144] => 0.73[145] => 0.73[146] => 0.73[147] => 0.73[148] => 0.73[149] => 0.73[150] => 0.73[151] => 0.73[152] => 0.73[153] => 0.73[154] => 0.73[155] => 0.73[156] => 0.73[157] => 0.72[158] => 0.72[159] => 0.72[160] => 0.72[161] => 0.72[162] => 0.72[163] => 0.72[164] => 0.72[165] => 0.72[166] => 0.72[167] => 0.72[168] => 0.72[169] => 0.72[170] => 0.72[171] => 0.72[172] => 0.72[173] => 0.72[174] => 0.72[175] => 0.72[176] => 0.72[177] => 0.72[178] => 0.72[179] => 0.72[180] => 0.71[181] => 0.71[182] => 0.71[183] => 0.71[184] => 0.71[185] => 0.71[186] => 0.71[187] => 0.71[188] => 0.71[189] => 0.71[190] => 0.71[191] => 0.71[192] => 0.71[193] => 0.71[194] => 0.71[195] => 0.71[196] => 0.71[197] => 0.71[198] => 0.71[199] => 0.71[200] => 0.71[201] => 0.71[202] => 0.71[203] => 0.7[204] => 0.7[205] => 0.7[206] => 0.7[207] => 0.7[208] => 0.7[209] => 0.7[210] => 0.7[211] => 0.7[212] => 0.7[213] => 0.7[214] => 0.7[215] => 0.7[216] => 0.7[217] => 0.7[218] => 0.7[219] => 0.7[220] => 0.7[221] => 0.7[222] => 0.7[223] => 0.7[224] => 0.7[225] => 0.69[226] => 0.69[227] => 0.69[228] => 0.69[229] => 0.69[230] => 0.69[231] => 0.69[232] => 0.69[233] => 0.69[234] => 0.69[235] => 0.69[236] => 0.69[237] => 0.69[238] => 0.69[239] => 0.69[240] => 0.69[241] => 0.69[242] => 0.69[243] => 0.69[244] => 0.69[245] => 0.69[246] => 0.69[247] => 0.68[248] => 0.68[249] => 0.68[250] => 0.68[251] => 0.68[252] => 0.68[253] => 0.68[254] => 0.68[255] => 0.68[256] => 0.68[257] => 0.68[258] => 0.68[259] => 0.68[260] => 0.68[261] => 0.68[262] => 0.68[263] => 0.68[264] => 0.68[265] => 0.68[266] => 0.68[267] => 0.68[268] => 0.68[269] => 0.67[270] => 0.67[271] => 0.67[272] => 0.67[273] => 0.67[274] => 0.67[275] => 0.67[276] => 0.67[277] => 0.67[278] => 0.67[279] => 0.67[280] => 0.67[281] => 0.67[282] => 0.67[283] => 0.67[284] => 0.67[285] => 0.67[286] => 0.67[287] => 0.67[288] => 0.67[289] => 0.67[290] => 0.66[291] => 0.66[292] => 0.66[293] => 0.66[294] => 0.66[295] => 0.66[296] => 0.66[297] => 0.66[298] => 0.66[299] => 0.66[300] => 0.66[301] => 0.66[302] => 0.66[303] => 0.66[304] => 0.66[305] => 0.66[306] => 0.66[307] => 0.66[308] => 0.66[309] => 0.66[310] => 0.66[311] => 0.66[312] => 0.65[313] => 0.65[314] => 0.65[315] => 0.65[316] => 0.65[317] => 0.65[318] => 0.65[319] => 0.65[320] => 0.65[321] => 0.65[322] => 0.65[323] => 0.65[324] => 0.65[325] => 0.65[326] => 0.65[327] => 0.65[328] => 0.65[329] => 0.65[330] => 0.65[331] => 0.65[332] => 0.64[333] => 0.64[334] => 0.64[335] => 0.64[336] => 0.64[337] => 0.64[338] => 0.64[339] => 0.64[340] => 0.64[341] => 0.64[342] => 0.64[343] => 0.64[344] => 0.64[345] => 0.64[346] => 0.64[347] => 0.64[348] => 0.64[349] => 0.64[350] => 0.64[351] => 0.64[352] => 0.64[353] => 0.63[354] => 0.63[355] => 0.63[356] => 0.63[357] => 0.63[358] => 0.63[359] => 0.63[360] => 0.63[361] => 0.63[362] => 0.63[363] => 0.63[364] => 0.63[365] => 0.63[366] => 0.63[367] => 0.63[368] => 0.63[369] => 0.63[370] => 0.63[371] => 0.63[372] => 0.63[373] => 0.62[374] => 0.62[375] => 0.62[376] => 0.62[377] => 0.62[378] => 0.62[379] => 0.62[380] => 0.62[381] => 0.62[382] => 0.62[383] => 0.62[384] => 0.62[385] => 0.62[386] => 0.62[387] => 0.62[388] => 0.62[389] => 0.62[390] => 0.62[391] => 0.62[392] => 0.62[393] => 0.61[394] => 0.61[395] => 0.61[396] => 0.61[397] => 0.61[398] => 0.61[399] => 0.61[400] => 0.61[401] => 0.61[402] => 0.61[403] => 0.61[404] => 0.61[405] => 0.61[406] => 0.61[407] => 0.61[408] => 0.61[409] => 0.61[410] => 0.61[411] => 0.61[412] => 0.61[413] => 0.6[414] => 0.6[415] => 0.6[416] => 0.6[417] => 0.6[418] => 0.6[419] => 0.6[420] => 0.6[421] => 0.6[422] => 0.6[423] => 0.6[424] => 0.6[425] => 0.6[426] => 0.6[427] => 0.6[428] => 0.6[429] => 0.6[430] => 0.6[431] => 0.6[432] => 0.59[433] => 0.59[434] => 0.59[435] => 0.59[436] => 0.59[437] => 0.59[438] => 0.59[439] => 0.59[440] => 0.59[441] => 0.59[442] => 0.59[443] => 0.59[444] => 0.59[445] => 0.59[446] => 0.59[447] => 0.59[448] => 0.59[449] => 0.59[450] => 0.59[451] => 0.58[452] => 0.58[453] => 0.58[454] => 0.58[455] => 0.58[456] => 0.58[457] => 0.58[458] => 0.58[459] => 0.58[460] => 0.58[461] => 0.58[462] => 0.58[463] => 0.58[464] => 0.58[465] => 0.58[466] => 0.58[467] => 0.58[468] => 0.58[469] => 0.57[470] => 0.57[471] => 0.57[472] => 0.57[473] => 0.57[474] => 0.57[475] => 0.57[476] => 0.57[477] => 0.57[478] => 0.57[479] => 0.57[480] => 0.57[481] => 0.57[482] => 0.57[483] => 0.57[484] => 0.57[485] => 0.57[486] => 0.57[487] => 0.57[488] => 0.56[489] => 0.56[490] => 0.56[491] => 0.56[492] => 0.56[493] => 0.56[494] => 0.56[495] => 0.56[496] => 0.56[497] => 0.56[498] => 0.56[499] => 0.56[500] => 0.56[501] => 0.56[502] => 0.56[503] => 0.56[504] => 0.56[505] => 0.56[506] => 0.55[507] => 0.55[508] => 0.55[509] => 0.55[510] => 0.55[511] => 0.55[512] => 0.55[513] => 0.55[514] => 0.55[515] => 0.55[516] => 0.55[517] => 0.55[518] => 0.55[519] => 0.55[520] => 0.55[521] => 0.55[522] => 0.55[523] => 0.54[524] => 0.54[525] => 0.54[526] => 0.54[527] => 0.54[528] => 0.54[529] => 0.54[530] => 0.54[531] => 0.54[532] => 0.54[533] => 0.54[534] => 0.54[535] => 0.54[536] => 0.54[537] => 0.54[538] => 0.54[539] => 0.54[540] => 0.54[541] => 0.53[542] => 0.53[543] => 0.53[544] => 0.53[545] => 0.53[546] => 0.53[547] => 0.53[548] => 0.53[549] => 0.53[550] => 0.53[551] => 0.53[552] => 0.53[553] => 0.53[554] => 0.53[555] => 0.53[556] => 0.53[557] => 0.53[558] => 0.52[559] => 0.52[560] => 0.52[561] => 0.52[562] => 0.52[563] => 0.52[564] => 0.52[565] => 0.52[566] => 0.52[567] => 0.52[568] => 0.52[569] => 0.52[570] => 0.52[571] => 0.52[572] => 0.52[573] => 0.52[574] => 0.51[575] => 0.51[576] => 0.51[577] => 0.51[578] => 0.51[579] => 0.51[580] => 0.51[581] => 0.51[582] => 0.51[583] => 0.51[584] => 0.51[585] => 0.51[586] => 0.51[587] => 0.51[588] => 0.51[589] => 0.51[590] => 0.51[591] => 0.5[592] => 0.5[593] => 0.5[594] => 0.5[595] => 0.5[596] => 0.5[597] => 0.5[598] => 0.5[599] => 0.5[600] => 0.5[601] => 0.5[602] => 0.5[603] => 0.5[604] => 0.5[605] => 0.5[606] => 0.5[607] => 0.49[608] => 0.49[609] => 0.49[610] => 0.49[611] => 0.49[612] => 0.49[613] => 0.49[614] => 0.49[615] => 0.49[616] => 0.49[617] => 0.49[618] => 0.49[619] => 0.49[620] => 0.49[621] => 0.49[622] => 0.48[623] => 0.48[624] => 0.48[625] => 0.48[626] => 0.48[627] => 0.48[628] => 0.48[629] => 0.48[630] => 0.48[631] => 0.48[632] => 0.48[633] => 0.48[634] => 0.48[635] => 0.48[636] => 0.48[637] => 0.48[638] => 0.47[639] => 0.47[640] => 0.47[641] => 0.47[642] => 0.47[643] => 0.47[644] => 0.47[645] => 0.47[646] => 0.47[647] => 0.47[648] => 0.47[649] => 0.47[650] => 0.47[651] => 0.47[652] => 0.47[653] => 0.46[654] => 0.46[655] => 0.46[656] => 0.46[657] => 0.46[658] => 0.46[659] => 0.46[660] => 0.46[661] => 0.46[662] => 0.46[663] => 0.46[664] => 0.46[665] => 0.46[666] => 0.46[667] => 0.46[668] => 0.45[669] => 0.45[670] => 0.45[671] => 0.45[672] => 0.45[673] => 0.45[674] => 0.45[675] => 0.45[676] => 0.45[677] => 0.45[678] => 0.45[679] => 0.45[680] => 0.45[681] => 0.45[682] => 0.44[683] => 0.44[684] => 0.44[685] => 0.44[686] => 0.44[687] => 0.44[688] => 0.44[689] => 0.44[690] => 0.44[691] => 0.44[692] => 0.44[693] => 0.44[694] => 0.44[695] => 0.44[696] => 0.43[697] => 0.43[698] => 0.43[699] => 0.43[700] => 0.43[701] => 0.43[702] => 0.43[703] => 0.43[704] => 0.43[705] => 0.43[706] => 0.43[707] => 0.43[708] => 0.43[709] => 0.43[710] => 0.42[711] => 0.42[712] => 0.42[713] => 0.42[714] => 0.42[715] => 0.42[716] => 0.42[717] => 0.42[718] => 0.42[719] => 0.42[720] => 0.42[721] => 0.42[722] => 0.42[723] => 0.41[724] => 0.41[725] => 0.41[726] => 0.41[727] => 0.41[728] => 0.41[729] => 0.41[730] => 0.41[731] => 0.41[732] => 0.41[733] => 0.41[734] => 0.41[735] => 0.41[736] => 0.41[737] => 0.4[738] => 0.4[739] => 0.4[740] => 0.4[741] => 0.4[742] => 0.4[743] => 0.4[744] => 0.4[745] => 0.4[746] => 0.4[747] => 0.4[748] => 0.4[749] => 0.39[750] => 0.39[751] => 0.39[752] => 0.39[753] => 0.39[754] => 0.39[755] => 0.39[756] => 0.39[757] => 0.39[758] => 0.39[759] => 0.39[760] => 0.39[761] => 0.39[762] => 0.38[763] => 0.38[764] => 0.38[765] => 0.38[766] => 0.38[767] => 0.38[768] => 0.38[769] => 0.38[770] => 0.38[771] => 0.38[772] => 0.38[773] => 0.38[774] => 0.37[775] => 0.37[776] => 0.37[777] => 0.37[778] => 0.37[779] => 0.37[780] => 0.37[781] => 0.37[782] => 0.37[783] => 0.37[784] => 0.37[785] => 0.37[786] => 0.36[787] => 0.36[788] => 0.36[789] => 0.36[790] => 0.36[791] => 0.36[792] => 0.36[793] => 0.36[794] => 0.36[795] => 0.36[796] => 0.36[797] => 0.36[798] => 0.35[799] => 0.35[800] => 0.35[801] => 0.35[802] => 0.35[803] => 0.35[804] => 0.35[805] => 0.35[806] => 0.35[807] => 0.35[808] => 0.35[809] => 0.34[810] => 0.34[811] => 0.34[812] => 0.34[813] => 0.34[814] => 0.34[815] => 0.34[816] => 0.34[817] => 0.34[818] => 0.34[819] => 0.34[820] => 0.33[821] => 0.33[822] => 0.33[823] => 0.33[824] => 0.33[825] => 0.33[826] => 0.33[827] => 0.33[828] => 0.33[829] => 0.33[830] => 0.32[831] => 0.32[832] => 0.32[833] => 0.32[834] => 0.32[835] => 0.32[836] => 0.32[837] => 0.32[838] => 0.32[839] => 0.32[840] => 0.31[841] => 0.31[842] => 0.31[843] => 0.31[844] => 0.31[845] => 0.31[846] => 0.31[847] => 0.31[848] => 0.31[849] => 0.31[850] => 0.3[851] => 0.3[852] => 0.3[853] => 0.3[854] => 0.3[855] => 0.3[856] => 0.3[857] => 0.3[858] => 0.3[859] => 0.3[860] => 0.29[861] => 0.29[862] => 0.29[863] => 0.29[864] => 0.29[865] => 0.29[866] => 0.29[867] => 0.29[868] => 0.29[869] => 0.28[870] => 0.28[871] => 0.28[872] => 0.28[873] => 0.28[874] => 0.28[875] => 0.28[876] => 0.28[877] => 0.28[878] => 0.27[879] => 0.27[880] => 0.27[881] => 0.27[882] => 0.27[883] => 0.27[884] => 0.27[885] => 0.27[886] => 0.27[887] => 0.26[888] => 0.26[889] => 0.26[890] => 0.26[891] => 0.26[892] => 0.26[893] => 0.26[894] => 0.26[895] => 0.26[896] => 0.25[897] => 0.25[898] => 0.25[899] => 0.25[900] => 0.25[901] => 0.25[902] => 0.25[903] => 0.24[904] => 0.24[905] => 0.24[906] => 0.24[907] => 0.24[908] => 0.24[909] => 0.24[910] => 0.24[911] => 0.23[912] => 0.23[913] => 0.23[914] => 0.23[915] => 0.23[916] => 0.23[917] => 0.23[918] => 0.23[919] => 0.22[920] => 0.22[921] => 0.22[922] => 0.22[923] => 0.22[924] => 0.22[925] => 0.22[926] => 0.21[927] => 0.21[928] => 0.21[929] => 0.21[930] => 0.21[931] => 0.21[932] => 0.2[933] => 0.2[934] => 0.2[935] => 0.2[936] => 0.2[937] => 0.2[938] => 0.2[939] => 0.19[940] => 0.19[941] => 0.19[942] => 0.19[943] => 0.19[944] => 0.19[945] => 0.18[946] => 0.18[947] => 0.18[948] => 0.18[949] => 0.18[950] => 0.18[951] => 0.17[952] => 0.17[953] => 0.17[954] => 0.17[955] => 0.17[956] => 0.16[957] => 0.16[958] => 0.16[959] => 0.16[960] => 0.16[961] => 0.15[962] => 0.15[963] => 0.15[964] => 0.15[965] => 0.15[966] => 0.14[967] => 0.14[968] => 0.14[969] => 0.14[970] => 0.14[971] => 0.13[972] => 0.13[973] => 0.13[974] => 0.13[975] => 0.12[976] => 0.12[977] => 0.12[978] => 0.11[979] => 0.11[980] => 0.11[981] => 0.11[982] => 0.1[983] => 0.1[984] => 0.1[985] => 0.09[986] => 0.09[987] => 0.09[988] => 0.08[989] => 0.08[990] => 0.08[991] => 0.07[992] => 0.07[993] => 0.06[994] => 0.06[995] => 0.05[996] => 0.05[997] => 0.04[998] => 0.02[999] => 0.01
)*/?>

抽奖砍价之类以均值为基础的波动算法 demo版本相关推荐

OpenCV均值漂移meanshift algorithm算法的实例(附完整代码)
OpenCV均值漂移meanshift algorithm算法的实例 OpenCV均值漂移meanshift algorithm算法的实例 OpenCV均值漂移meanshift algorithm算 ...
【定位问题】基于RSSI均值的等边三角形定位算法matlab系统
1 简介为了提高无线传感网络的定位精度从提高测量精度.改善信标N点分布的角度提出了一种基于RSSI均值的等边三角形定位算法.该算法引入信号强指( RSSI)敏感区和非敏感区概念采用高斯模型对非敏感区 ...
聚类之K均值聚类和EM算法
这篇博客整理K均值聚类的内容,包括: 1.K均值聚类的原理: 2.初始类中心的选择和类别数K的确定: 3.K均值聚类和EM算法.高斯混合模型的关系. 一.K均值聚类的原理 K均值聚类(K-means) ...
echarts中toolbox位置_基于QGIS中的LSMS（大规模均值漂移）分割算法
在学习分割算法中,很多使用的是易康(eCognition)中的分割算法,在这里仅介绍一个开源软件的大规模均值漂移算法,在Remote Sensing期刊中也有用到.希望此文给与一定帮助.感兴趣可以阅读 ...
k均值聚类算法python_K均值和其他聚类算法：Python快速入门
k均值聚类算法python This post was originally published here 这篇文章最初发表在这里 Clustering is the grouping of obje ...
K均值分类做多光谱图像分割matlab版本
K均值分类算法已经比较常见了,但还是值得学习一下,毕竟这个算法的框架很好,在此基础上可以更新一下.网上有很多的讲解和程序,我也看了一下毕竟自己做了,还是想和大家分享一下代码,在这里还是献丑了 matl ...
原神服务器维护后抽奖池会更新吗,原神：1.1版本上线四角色更新，抽奖池要出双up？...
昨天原神的1.1版本直播更新内容相信很多玩家都怀着热情去看了,结果却是让很多玩家失望,因为更新的内容实在是有点少. 最重要的更新就是4个角色的更新了,其中男角色达达利亚和钟离,都是非常受女玩家喜爱的. ...
基于FCM模糊C均值的数据聚类算法matlab仿真
目录 1.算法仿真效果 2.MATLAB源码 3.算法概述 4.部分参考文献 1.算法仿真效果 matlab2022a仿真结果如下:
K-Means算法Demo
简介:本Demo是参照这个网站上的Demo自己用Java实现的.将Java打包为Jar,再将Jar转为exe,源代码及程序Demo下载请点我. K-Means算法简介我尽量用通俗易懂但不规范的语言来 ...

抽奖砍价之类以均值为基础的波动算法 demo版本

抽奖砍价之类以均值为基础的波动算法 demo版本相关推荐

最新文章

热门文章