/*
gcc -g hiseiki3.cpp -o hiseiki3
*/
/*
まず「基本形」を崩す
非正規社員投入の方針は、
(1)潜在的な市場が増加中なら投入
(2)赤字に転じたら、直ちに非正規社員を全員解雇
という極めて単純なもの
に、
非正規社員を保護する
最低3年間は解雇できないもの、としてみる
*/
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
typedef struct person{
int age; // 20歳～60歳 のいずれか
int position; // 1:正規社員 0:非正規社員
double productivity; // 生産力 正規社員の場合
///////////
struct person *prev; /* 前の構造体を示すポインタ */
struct person *next; /* 次の構造体を示すポインタ */
} PERSON;
typedef struct company{
int payroll;
double productivity; // 会社としての生産力の合計
} COMPANY;
PERSON *p_first_person, *p_last_person; //社員リスト(あえてグローバルで保持)
// 社員用リストの先頭と終端を作成するルーチン
/*
リストの先頭と終端をはグローバルでも良いのであるが、
一応、メインルーチンの方で、陽に定義できるように、
ここでは、返り値としている
*/
double min(double a, double b){
if (b > a)
return a;
else
return b;
};
double juglar_cycles(int year)
{
// 10年で変動するsin周期
// ベースとするのは2017年とする(単なる仮説)
int x = (year -7) % 10;
double y = sin( (double)x /10.0 * 2 * 3.141592654);
return y;
}
void init_person_list(PERSON **p_first_person, PERSON **p_last_person)
{
PERSON *p_top_person = (PERSON *)malloc(sizeof(PERSON));
if(p_top_person == NULL) {
printf("メモリが確保できません\n");
exit(EXIT_FAILURE);
}
memset(p_top_person, 0, sizeof(PERSON)); // ゼロクリア
PERSON *p_tail_person = (PERSON *)malloc(sizeof(PERSON));
if(p_tail_person == NULL) {
printf("メモリが確保できません\n");
exit(EXIT_FAILURE);
}
memset(p_tail_person, 0, sizeof(PERSON)); // ゼロクリア
*p_first_person = p_top_person;
*p_last_person = p_tail_person;
(*p_first_person)->prev = NULL;
(*p_last_person)->next = NULL;
(*p_first_person)->next = (*p_last_person);
(*p_last_person)->prev = (*p_first_person);
return;
}
// 社員オブジェクトを生成して、社員用リストに追加するルーチン
void add_person(PERSON *p_ref_person)
{
PERSON *new_p_person = (PERSON *)malloc(sizeof(PERSON));
if(new_p_person == NULL) {
printf("メモリが確保できません\n");
exit(EXIT_FAILURE);
}
memset(new_p_person, 0, sizeof(PERSON)); // ゼロクリア
memcpy(new_p_person, p_ref_person, sizeof(PERSON)); // 引数の動的メモリの内容コピー
// personの追加属性記述ここから
// personの追加属性記述ここまで
PERSON *p_person = p_last_person->prev;
p_person->next = new_p_person;
new_p_person->prev = p_person;
p_last_person->prev = new_p_person;
new_p_person->next = p_last_person;
return;
}
void delete_person(PERSON *p_person)
{
// ポインタを貼り替えて
p_person->prev->next = p_person->next;
p_person->next->prev = p_person->prev;
// そのメモリを解放する
free(p_person);
return;
}
int main()
{
double potential = juglar_cycles(2017) * 1500.0 + 7500.0;
double border = 7000.0; // 利益のボーダー(固定と考える)
COMPANY company;
company.productivity = 7437.500000; // 生産力初期値
// 社員格納用リストの作成
init_person_list(&p_first_person, &p_last_person);
//20歳から59歳までの正規社員、各世代100人づつ、合計4000人を作成する
/////// PERSONを作成する ///////
for (int age = 20; age < 60; age ++){
for (int i = 0; i < 100; i++){
PERSON person;
person.age = age;
person.position = 1; // 1:正規社員 0:非正規社員
person.productivity = 1.25 + (2.50 -1.25) / 40.0 * ((double)age -20.0); //20歳 1.25 60歳 2.50まで線形変化
add_person(&person);
}
}
/////// PERSONを作成する ///////
double sum_profit = 0;
for ( int year = 2017; year < 2040; year++){
//// 年齢を1歳加算し、定年
// ===== PERSON ループを回す========
PERSON* p_person = p_first_person->next;
while (p_person != p_last_person){
p_person->age += 1;
if (p_person->position == 1){ // 正規社員であれば、生産力は年齢とともに高くなる(という仮説)
p_person->productivity = 1.25 + (2.50 -1.25) / 40.0 * ((double)(p_person->age) -20.0); //20歳 1.25 60歳 2.50まで線形変化
}
else if (p_person->position == 0){ // 非正規社員であれば、生産力は年齢と関係なく高くならない(という仮説)
p_person->productivity = 1.0;
}
if (p_person->age >= 60){ // 60歳になったら定年(正規社員だろうが、非正規社員だろうが)
delete_person(p_person);
}
p_person = p_person->next;
}
// ===== PERSON ループを回す(ここまで)========
//// 100人の20歳の新入社員を入社する
for (int i = 0; i < 100; i++){
/////// PERSONを作成する ///////
PERSON person;
person.age = 20;
person.position = 1; // 1:正規社員 0:非正規社員
person.productivity = 1.25 + (2.50 -1.25) / 40.0 * ((double)person.age -20.0); //20歳 1.25 60歳 2.50まで線形変化
add_person(&person);
}
#if 1
// 昨年の景気ラインと昨年の生産力を比較して、景気ライン > 昨年の生産力 となっていたら、非正規社員を必要分だけ投入する
int add_person_number;
if (potential > company.productivity){
add_person_number = (int)(potential - company.productivity);
}
// printf("potential = %f, company.productivity = %f, add_person_number=%d\n",potential, company.productivity, add_person_number);
for (int i=0; i < add_person_number; i++){
PERSON person;
person.age = 20; // 若い奴を突っ込むことにする
person.position = 0; // 1:正規社員 0:非正規社員
person.productivity = 1.0; // 生産力は"1.0"で固定、かつ年齢とともに成長しない)
add_person(&person);
}
// 昨年の景気ラインと利益のボーダを比較して 、景気ライン < 利益のボーダ となっていたら、非正規社員を全て即時に解雇する
if (potential - border < 0.0){
// ===== PERSON ループを回す========
p_person = p_first_person->next;
while (p_person != p_last_person){
if (p_person->position == 0){ // 非正規社員
if (p_person->age > 25){
delete_person(p_person);
}
}
p_person = p_person->next;
}
// ===== PERSON ループを回す(ここまで)========
}
#endif //0
potential = juglar_cycles(year) * 1500.0 + 7500.0; // 景気ライン(ジャグラーサイクル)の更新
/////////// ここから生産力算出ループ
company.productivity = 0; // 生産力ゼロリセット
company.payroll = 0 ; // 従業員数ゼロリセット
// ===== PERSON ループを回す========
p_person = p_first_person->next;
while (p_person != p_last_person){
company.payroll += 1 ; // 従業員数一人加算
company.productivity += p_person->productivity; // 生産力加算
p_person = p_person->next;
}
// ===== PERSON ループを回す(ここまで)========
//printf("company.payroll = %d\n",company.payroll); // company.payroll = 4000
//printf("company.productivity = %f\n",company.productivity); // company.productivity = 7437.500000
double profit = min(company.productivity, potential) - border;
sum_profit += profit;
//printf("year = %d company.productivity = %f potential = %f profit = %f sum_profit=%f payroll=%d\n",year,company.productivity, potential, profit,sum_profit,company.payroll);
printf("%d,%f,%f,%f,%f,%d\n",year,company.productivity, potential, profit,sum_profit,company.payroll);
} // for ( int year = 2017; year < 2040; year++){
return 0;
}非正規社員シミュレーション
1992年生まれから人口の100%がディジタルネイティブになったと仮定する。Q:日本のデジタルネイティブの比率は、どう変化していくだろうか
2/*
gcc -g birth_digital_native.cpp -o birth_digital_native
*/
/*
デジタルネイティブ
1992年生まれから人口の100%がディジタルネイティブになったと仮定する。Q:日本のデジタルネイティブの比率は、どう変化していくだろうか
*/
#include "stdio.h"
int main(int argc, char* argv[])
{
double men[101],women[101]; // 年齢別人口 平成22年データ 単位は1000人
double men_death_rate[101],women_death_rate[101]; // 死亡率 平成22年データ (資料 厚生労働省大臣官房統計情報部人口動態・保健統計課「人口動態統計」)
// ファイルデバイスとデータ形式の統一回避する為、データべた書き
men[ 0]=549 ; men_death_rate[ 0]=2.5/1000.0 ; // 男性0歳人口549千人、死亡率0.25% 2012年生まれ
men[ 1]=535 ; men_death_rate[ 1]=0.4/1000.0 ; //2011年生まれ
men[ 2]=535 ; men_death_rate[ 2]=0.2/1000.0 ; //2010年生まれ
men[ 3]=550 ; men_death_rate[ 3]=0.2/1000.0 ;
men[ 4]=548 ; men_death_rate[ 4]=0.2/1000.0 ;
men[ 5]=544 ; men_death_rate[ 5]=0.1/1000.0 ;
men[ 6]=542 ; men_death_rate[ 6]=0.1/1000.0 ;
men[ 7]=562 ; men_death_rate[ 7]=0.1/1000.0 ;
men[ 8]=574 ; men_death_rate[ 8]=0.1/1000.0 ;
men[ 9]=589 ; men_death_rate[ 9]=0.1/1000.0 ; //2003年生まれ
men[10]=597 ; men_death_rate[10]=0.1/1000.0 ; //2002年生まれ
men[11]=604 ; men_death_rate[11]=0.1/1000.0 ;
men[12]=604 ; men_death_rate[12]=0.1/1000.0 ;
men[13]=613 ; men_death_rate[13]=0.1/1000.0 ;
men[14]=610 ; men_death_rate[14]=0.1/1000.0 ;
men[15]=607 ; men_death_rate[15]=0.3/1000.0 ;
men[16]=627 ; men_death_rate[16]=0.3/1000.0 ;
men[17]=632 ; men_death_rate[17]=0.3/1000.0 ;
men[18]=621 ; men_death_rate[18]=0.3/1000.0 ; //1990年生まれ (ここまでが、ディタルネイティブ)
men[19]=631 ; men_death_rate[19]=0.3/1000.0 ;
men[20]=623 ; men_death_rate[20]=0.6/1000.0 ; //1992年生まれ
men[21]=632 ; men_death_rate[21]=0.6/1000.0 ;
men[22]=648 ; men_death_rate[22]=0.6/1000.0 ;
men[23]=668 ; men_death_rate[23]=0.6/1000.0 ;
men[24]=683 ; men_death_rate[24]=0.6/1000.0 ;
men[25]=697 ; men_death_rate[25]=0.7/1000.0 ;
men[26]=723 ; men_death_rate[26]=0.7/1000.0 ;
men[27]=745 ; men_death_rate[27]=0.7/1000.0 ;
men[28]=754 ; men_death_rate[28]=0.7/1000.0 ;
men[29]=754 ; men_death_rate[29]=0.7/1000.0 ;
men[30]=764 ; men_death_rate[30]=0.8/1000.0 ;
men[31]=797 ; men_death_rate[31]=0.8/1000.0 ;
men[32]=818 ; men_death_rate[32]=0.8/1000.0 ;
men[33]=852 ; men_death_rate[33]=0.8/1000.0 ;
men[34]=873 ; men_death_rate[34]=0.8/1000.0 ;
men[35]=917 ; men_death_rate[35]=1.0/1000.0 ;
men[36]=960 ; men_death_rate[36]=1.0/1000.0 ;
men[37]=1012 ; men_death_rate[37]=1.0/1000.0 ;
men[38]=1028 ; men_death_rate[38]=1.0/1000.0 ;
men[39]=1010 ; men_death_rate[39]=1.0/1000.0 ;
men[40]=982 ; men_death_rate[40]=1.5/1000.0 ;
men[41]=954 ; men_death_rate[41]=1.5/1000.0 ;
men[42]=937 ; men_death_rate[42]=1.5/1000.0 ;
men[43]=916 ; men_death_rate[43]=1.5/1000.0 ;
men[44]=915 ; men_death_rate[44]=1.5/1000.0 ;
men[45]=713 ; men_death_rate[45]=2.4/1000.0 ;
men[46]=882 ; men_death_rate[46]=2.4/1000.0 ;
men[47]=826 ; men_death_rate[47]=2.4/1000.0 ;
men[48]=805 ; men_death_rate[48]=2.4/1000.0 ;
men[49]=778 ; men_death_rate[49]=2.4/1000.0 ;
men[50]=765 ; men_death_rate[50]=3.8/1000.0 ;
men[51]=770 ; men_death_rate[51]=3.8/1000.0 ;
men[52]=783 ; men_death_rate[52]=3.8/1000.0 ;
men[53]=761 ; men_death_rate[53]=3.8/1000.0 ;
men[54]=740 ; men_death_rate[54]=3.8/1000.0 ;
men[55]=776 ; men_death_rate[55]=6.3/1000.0 ;
men[56]=803 ; men_death_rate[56]=6.3/1000.0 ;
men[57]=803 ; men_death_rate[57]=6.3/1000.0 ;
men[58]=850 ; men_death_rate[58]=6.3/1000.0 ;
men[59]=896 ; men_death_rate[59]=6.3/1000.0 ;
men[60]=949 ; men_death_rate[60]=9.3/1000.0 ;
men[61]=1018 ; men_death_rate[61]=9.3/1000.0 ;
men[62]=1111 ; men_death_rate[62]=9.3/1000.0 ;
men[63]=1099 ; men_death_rate[63]=9.3/1000.0 ;
men[64]=1042 ; men_death_rate[64]=9.3/1000.0 ;
men[65]=645 ; men_death_rate[65]=14.6/1000.0 ;
men[66]=684 ; men_death_rate[66]=14.6/1000.0 ;
men[67]=825 ; men_death_rate[67]=14.6/1000.0 ;
men[68]=794 ; men_death_rate[68]=14.6/1000.0 ;
men[69]=809 ; men_death_rate[69]=14.6/1000.0 ;
men[70]=780 ; men_death_rate[70]=22.7/1000.0 ;
men[71]=698 ; men_death_rate[71]=22.7/1000.0 ;
men[72]=599 ; men_death_rate[72]=22.7/1000.0 ;
men[73]=627 ; men_death_rate[73]=22.7/1000.0 ;
men[74]=631 ; men_death_rate[74]=22.7/1000.0 ;
men[75]=616 ; men_death_rate[75]=39.6/1000.0 ;
men[76]=571 ; men_death_rate[76]=39.6/1000.0 ;
men[77]=521 ; men_death_rate[77]=39.6/1000.0 ;
men[78]=501 ; men_death_rate[78]=39.6/1000.0 ;
men[79]=470 ; men_death_rate[79]=39.6/1000.0 ;
men[80]=430 ; men_death_rate[80]=70.5/1000.0 ;
men[81]=385 ; men_death_rate[81]=70.5/1000.0 ;
men[82]=350 ; men_death_rate[82]=70.5/1000.0 ;
men[83]=316 ; men_death_rate[83]=70.5/1000.0 ;
men[84]=281 ; men_death_rate[84]=70.5/1000.0 ;
men[85]=247 ; men_death_rate[85]=120.3/1000.0 ;
men[86]=202 ; men_death_rate[86]=120.3/1000.0 ;
men[87]=158 ; men_death_rate[87]=120.3/1000.0 ;
men[88]=122 ; men_death_rate[88]=120.3/1000.0 ;
men[89]=98 ; men_death_rate[89]=120.3/1000.0 ;
men[90]=78 ; men_death_rate[90]=202.5/1000.0 ;
men[91]=67 ; men_death_rate[91]=202.5/1000.0 ;
men[92]=44 ; men_death_rate[92]=202.5/1000.0 ;
men[93]=36 ; men_death_rate[93]=202.5/1000.0 ;
men[94]=28 ; men_death_rate[94]=202.5/1000.0 ;
men[95]=21 ; men_death_rate[95]=318.8/1000.0 ;
men[96]=15 ; men_death_rate[96]=318.8/1000.0 ;
men[97]=11 ; men_death_rate[97]=318.8/1000.0 ;
men[98]=7 ; men_death_rate[98]=318.8/1000.0 ;
men[99]=5 ; men_death_rate[99]=318.8/1000.0 ;
women[ 0]=520; women_death_rate[ 0]=2.1/1000.0 ;// 女性0歳人口520千人、死亡率0.21%
women[ 1]=510; women_death_rate[ 1]=0.4/1000.0 ;
women[ 2]=511; women_death_rate[ 2]=0.2/1000.0 ;
women[ 3]=525; women_death_rate[ 3]=0.1/1000.0 ;
women[ 4]=522; women_death_rate[ 4]=0.1/1000.0 ;
women[ 5]=518; women_death_rate[ 5]=0.1/1000.0 ;
women[ 6]=517; women_death_rate[ 6]=0.1/1000.0 ;
women[ 7]=538; women_death_rate[ 7]=0.1/1000.0 ;
women[ 8]=545; women_death_rate[ 8]=0.1/1000.0 ;
women[ 9]=561; women_death_rate[ 9]=0.1/1000.0 ;
women[10]=568; women_death_rate[10]=0.1/1000.0 ;
women[11]=573; women_death_rate[11]=0.1/1000.0 ;
women[12]=576; women_death_rate[12]=0.1/1000.0 ;
women[13]=585; women_death_rate[13]=0.1/1000.0 ;
women[14]=583; women_death_rate[14]=0.1/1000.0 ;
women[15]=578; women_death_rate[15]=0.2/1000.0 ;
women[16]=595; women_death_rate[16]=0.2/1000.0 ;
women[17]=597; women_death_rate[17]=0.2/1000.0 ;
women[18]=589; women_death_rate[18]=0.2/1000.0 ;//1990年生まれ (ここまでが、ディタルネイティブ)
women[19]=599; women_death_rate[19]=0.2/1000.0 ;
women[20]=596; women_death_rate[20]=0.3/1000.0 ;
women[21]=605; women_death_rate[21]=0.3/1000.0 ;
women[22]=622; women_death_rate[22]=0.3/1000.0 ;
women[23]=638; women_death_rate[23]=0.3/1000.0 ;
women[24]=655; women_death_rate[24]=0.3/1000.0 ;
women[25]=667; women_death_rate[25]=0.3/1000.0 ;
women[26]=697; women_death_rate[26]=0.3/1000.0 ;
women[27]=719; women_death_rate[27]=0.3/1000.0 ;
women[28]=729; women_death_rate[28]=0.3/1000.0 ;
women[29]=734; women_death_rate[29]=0.3/1000.0 ;
women[30]=742; women_death_rate[30]=0.4/1000.0 ;
women[31]=774; women_death_rate[31]=0.4/1000.0 ;
women[32]=794; women_death_rate[32]=0.4/1000.0 ;
women[33]=828; women_death_rate[33]=0.4/1000.0 ;
women[34]=849; women_death_rate[34]=0.4/1000.0 ;
women[35]=890; women_death_rate[35]=0.6/1000.0 ;
women[36]=931; women_death_rate[36]=0.6/1000.0 ;
women[37]=982; women_death_rate[37]=0.6/1000.0 ;
women[38]=1001; women_death_rate[38]=0.6/1000.0 ;
women[39]=981; women_death_rate[39]=0.6/1000.0 ;
women[40]=958; women_death_rate[40]=0.8/1000.0 ;
women[41]=931; women_death_rate[41]=0.8/1000.0 ;
women[42]=920; women_death_rate[42]=0.8/1000.0 ;
women[43]=902; women_death_rate[43]=0.8/1000.0 ;
women[44]=898; women_death_rate[44]=0.8/1000.0 ;
women[45]=705; women_death_rate[45]=1.3/1000.0 ;
women[46]=872; women_death_rate[46]=1.3/1000.0 ;
women[47]=815; women_death_rate[47]=1.3/1000.0 ;
women[48]=798; women_death_rate[48]=1.3/1000.0 ;
women[49]=772; women_death_rate[49]=1.3/1000.0 ;
women[50]=760; women_death_rate[50]=1.9/1000.0 ;
women[51]=768; women_death_rate[51]=1.9/1000.0 ;
women[52]=783; women_death_rate[52]=1.9/1000.0 ;
women[53]=765; women_death_rate[53]=1.9/1000.0 ;
women[54]=744; women_death_rate[54]=1.9/1000.0 ;
women[55]=783; women_death_rate[55]=2.8/1000.0 ;
women[56]=810; women_death_rate[56]=2.8/1000.0 ;
women[57]=813; women_death_rate[57]=2.8/1000.0 ;
women[58]=868; women_death_rate[58]=2.8/1000.0 ;
women[59]=918; women_death_rate[59]=2.8/1000.0 ;
women[60]=975; women_death_rate[60]=3.9/1000.0 ;
women[61]=1051; women_death_rate[61]=3.9/1000.0 ;
women[62]=1152; women_death_rate[62]=3.9/1000.0 ;
women[63]=1146; women_death_rate[63]=3.9/1000.0 ;
women[64]=1090; women_death_rate[64]=3.9/1000.0 ;
women[65]=685; women_death_rate[65]=6.0/1000.0 ;
women[66]=741; women_death_rate[66]=6.0/1000.0 ;
women[67]=903; women_death_rate[67]=6.0/1000.0 ;
women[68]=875; women_death_rate[68]=6.0/1000.0 ;
women[69]=899; women_death_rate[69]=6.0/1000.0 ;
women[70]=873; women_death_rate[70]=9.8/1000.0 ;
women[71]=793; women_death_rate[71]=9.8/1000.0 ;
women[72]=690; women_death_rate[72]=9.8/1000.0 ;
women[73]=738; women_death_rate[73]=9.8/1000.0 ;
women[74]=755; women_death_rate[74]=9.8/1000.0 ;
women[75]=753; women_death_rate[75]=17.9/1000.0 ;
women[76]=718; women_death_rate[76]=17.9/1000.0 ;
women[77]=675; women_death_rate[77]=17.9/1000.0 ;
women[78]=671; women_death_rate[78]=17.9/1000.0 ;
women[79]=646; women_death_rate[79]=17.9/1000.0 ;
women[80]=614; women_death_rate[80]=34.3/1000.0 ;
women[81]=573; women_death_rate[81]=34.3/1000.0 ;
women[82]=547; women_death_rate[82]=34.3/1000.0 ;
women[83]=515; women_death_rate[83]=34.3/1000.0 ;
women[84]=482; women_death_rate[84]=34.3/1000.0 ;
women[85]=454; women_death_rate[85]=69.1/1000.0 ;
women[86]=405; women_death_rate[86]=69.1/1000.0 ;
women[87]=349; women_death_rate[87]=69.1/1000.0 ;
women[88]=313; women_death_rate[88]=69.1/1000.0 ;
women[89]=276; women_death_rate[89]=69.1/1000.0 ;
women[90]=236; women_death_rate[90]=131.2/1000.0 ;
women[91]=213; women_death_rate[91]=131.2/1000.0 ;
women[92]=146; women_death_rate[92]=131.2/1000.0 ;
women[93]=128; women_death_rate[93]=131.2/1000.0 ;
women[94]=106; women_death_rate[94]=131.2/1000.0 ;
women[95]=87 ; women_death_rate[95]=238.1/1000.0 ;
women[96]=63 ; women_death_rate[96]=238.1/1000.0 ;
women[97]=49 ; women_death_rate[97]=238.1/1000.0 ;
women[98]=35 ; women_death_rate[98]=238.1/1000.0 ;
women[99]=25 ; women_death_rate[99]=238.1/1000.0 ;
for (int year = 2012; year < 2100; year++){ // 2012年から2100年までループ計算
double dummy = 0;
for(int i = 15; i < 50; i++){ // 特殊出産率の対象 15歳から49歳までの人口加算
dummy += women[i];
}
// 1.4は、特殊出生率 / 35は特殊出生率の対象期間(35年) / 1.05は男性の出生比率
double mem_new_birth = dummy * 1.4 / 35.0 * 1.05/(1.05+1.00);
double womem_new_birth = dummy * 1.4 / 35.0 * 1.00/(1.05+1.00);
// 1年単位の人口移動 (死亡率も考慮)
for (int k = 99; k >= 0; k--){
men[k+1] = men[k] * (1.0 - men_death_rate[k]);
women[k+1] = women[k] * (1.0 - women_death_rate[k]);
//printf("%d %f %f \n", k, men[k], women[k]);
}
// 新生児の人口を追加
men[0] = mem_new_birth;
women[0] = womem_new_birth;
// 人口総計(年齢99歳まで。100歳以上の人口は無視することにした)
double sum_men = 0;
double sum_women = 0;
for (int m = 0; m <= 100; m++){
sum_men += men[m];
sum_women += women[m];
}
#if 0
// ディタルネイティブ人口総計
double digital_sum_men = 0;
double digital_sum_women = 0;
int l = year -1990;
if (l >= 100) l = 100;
for (int n = 0; n <= l ; n++){
digital_sum_men += men[n];
digital_sum_women += women[n];
}
#endif
#if 0 // ここから江端仮説
// ディタルネイティブ人口総計
double digital_sum_men = 0;
double digital_sum_women = 0;
// 1970年以後の人は100%デジタルは使えるいう仮説の導入
int l = year -1970;
if (l >= 100) l = 100;
for (int n = 0; n <= l ; n++){
digital_sum_men += men[n];
digital_sum_women += women[n];
}
// 1950-70年にかけてデジタルを使える人は線形に増加した、という仮説の導入
// デジタルネイティブ人口(江端"補正"仮説)
int p1 = year -1970; // 例：2020年の時に50歳
int p2 = year -1950; // 例：2020年の時に70歳
for (int i = p1; i < p2 ; i++){
if (i < 100){
// 例：2020年の時に70歳の人の0%、 60歳の人の50%、50歳の人の100%がデジタルを扱えるとする
digital_sum_men += men[i] * 1.0 / (double)(p2 - p1) * (double)(p2 - i);
digital_sum_women += women[i] * 1.0 / (double)(p2 - p1) * (double)(p2 - i);
}
}
#endif
#if 1
// 高齢者世代(65歳以上)に特化して計算してみる
// 人口総計(年齢65歳から99歳まで。100歳以上の人口は無視することにした)
sum_men = 0;
sum_women = 0;
for (int m = 65; m <= 100; m++){
sum_men += men[m];
sum_women += women[m];
}
// ディタルネイティブ人口総計
double digital_sum_men = 0;
double digital_sum_women = 0;
// 1970年以後の人は100%デジタルは使えるいう仮説の導入
int l = year -1970;
if (l >= 100) l = 100;
for (int n = 65; n <= l ; n++){
digital_sum_men += men[n];
digital_sum_women += women[n];
}
// 1950-70年にかけてデジタルを使える人は線形に増加した、という仮説の導入
// デジタルネイティブ人口(江端"補正"仮説)
int p1 = year -1970; // 例：2020年の時に50歳 2017年の時に47歳
int p2 = year -1950; // 例：2020年の時に70歳 2017年の時に67歳
for (int i = p1; i < p2 ; i++){
if ((i >= 65) &&(i < 100)){
// 例：2020年の時に70歳の人の0%、 60歳の人の50%、50歳の人の100%がデジタルを扱えるとする
digital_sum_men += men[i] * 1.0 / (double)(p2 - p1) * (double)(p2 - i);
digital_sum_women += women[i] * 1.0 / (double)(p2 - p1) * (double)(p2 - i);
}
}
#endif
printf("%d,%f,%f,%f\n", year, sum_men + sum_women, digital_sum_men + digital_sum_women, (digital_sum_men + digital_sum_women)/(sum_men + sum_women) );
}
}
「上司の帰宅が遅れると、社員の帰宅も遅れる」の仮説検証プログラム
/*
g++ -g seat.cpp -o seat
*/
/*
「上司の帰宅が遅れると、社員の帰宅も遅れる」の仮説検証
*/
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
// 共通関数
double max(double a){
return a;
}
double max(double a, double b){
if (a > b)
return a;
else
return b;
};
double max(double a, double b, double c ){
return max(max(a,b), max(b,c));
};
double max(double a, double b, double c, double d ){
return max(max(a,b,c), d);
};
double min(double a){
return a;
}
double min(double a, double b){
if (b > a)
return a;
else
return b;
};
double min(double a, double b, double c ){
return min(min(a,b), min(b,c));
};
double min(double a, double b, double c, double d ){
return min(min(a,b,c), d);
};
// ファジィ表現
typedef enum scale {LESSLESS, LESS, ZERO, MORE, MOREMORE} SCALE;
// 前件部メンバーシップ関数(山3つ)クラス
class condition_MF3
{
private:
double center;
double width;
SCALE express;
public:
condition_MF3(double _center, double _witdth, SCALE _express){
center = _center;
width = _witdth;
express = _express;
// 使用できないファジィ表現を使った場合は止める
if ((express == LESSLESS) || (express == MOREMORE)){
printf("wrong expression used \n");
exit(0);
}
};
double func(double _x);
};
double condition_MF3::func(double _x)
{
// x,yは、メンバーシップ関数上の座標を示す
double x = _x;
double y = 0.0; // yの値は、必ず0以上1以下になる
if (express == LESS){
if (x <= center - width){
y = 1.0;
}
else if (x <= center){
y = - 1.0 / width * (x - center);
}
else{
y = 0.0;
}
}
else if (express == ZERO){
if (x <= center - width){
y = 0.0;
}
else if (x <= center){
y = 1.0 / width * (x - center) + 1.0;
}
else if (x <= center + width){
y = -1.0 / width * (x - center) + 1.0;
}
else{
y = 0.0;
}
}
else if (express == MORE){
if (x <= center){
y = 0.0;
}
else if (x <= center + width){
y = 1.0 / width * (x - center);
}
else{
y = 1.0;
}
}
else {
printf("wrong expression\n");
exit(1);
}
return y;
};
// 前件部メンバーシップ関数(山5つ)クラス
class condition_MF5
{
private:
double center;
double width;
SCALE express;
public:
condition_MF5(double _center, double _witdth, SCALE _express){
center = _center;
width = _witdth;
express = _express;
};
double func(double _x);
};
double condition_MF5::func(double _x)
{
// x,yは、メンバーシップ関数上の座標を示す
double x = _x;
double y = 0.0; // yの値は、必ず0以上1以下になる
if (express == LESSLESS){
if (x <= center - 2.0 * width){
y = 1.0;
}
else if (x <= center - width){
y = - 1.0 / width * (x - (center - 2.0 * width)) + 1.0;
}
else{
y = 0.0;
}
}
else if (express == LESS){
if (x <= center - 2.0 * width){
y = 0.0;
}
else if (x <= center - width){
y = 1.0 / width * (x - (center - width)) + 1.0;
}
else if (x <= center){
y = -1.0 / width * (x - (center - width)) + 1.0;
}
else{
y = 0.0;
}
}
else if (express == ZERO){
if (x <= center - width){
y = 0.0;
}
else if (x <= center){
y = 1.0 / width * (x - center) + 1.0;
}
else if (x <= center + width){
y = -1.0 / width * (x - center) + 1.0;
}
else{
y = 0.0;
}
}
else if (express == MORE){
if (x <= center){
y = 0.0;
}
else if (x <= center + width){
y = 1.0 / width * (x - (center + width)) + 1.0;
}
else if (x <= center + 2.0 * width){
y = -1.0 / width * (x - (center + width)) + 1.0;
}
else{
y = 0.0;
}
}
else if (express == MOREMORE){
if (x <= center + width){
y = 0.0;
}
else if (x <= center + 2.0 * width){
y = 1.0 / width * (x - (center + 2.0 * width)) + 1.0;
}
else{
y = 1.0;
}
}
return y;
};
// 後件部メンバーシップ関数(山3つ)クラス
class action_MF3
{
private:
double center;
double width;
SCALE express;
double x;
double y;
public:
action_MF3(double _center, double _witdth, SCALE _express){
y = 0.0; // yの値は、必ず0以上1以下になる
center = _center;
width = _witdth;
express = _express;
if (express == LESS){
x = center - width;
}
else if (express == ZERO){
x = center;
}
else if (express == MORE){
x = center + width;
}
else{
printf("wrong scale expression\n");
exit(0);
}
};
// Y座標の値を最大値で更新する
void func_Max(double b){
y = max(b, y);
};
// Y座標の値をリセット(y=0)する
void func_Reset(){
y = 0.0;
};
// X座標を返す
double func_X(void){
return x;
};
// (最大値で更新された、最後の)Y座標を返す
double func_Y(){
return y;
};
};
// 後件部メンバーシップ関数(山5つ)クラス
class action_MF5
{
private:
double center;
double width;
SCALE express;
double x;
double y;
public:
action_MF5(double _center, double _witdth, SCALE _express){
y = 0.0; // yの値は、必ず0以上1以下になる
center = _center;
width = _witdth;
express = _express;
if (express == LESSLESS){
x = center - 2.0 * width;
}
else if (express == LESS){
x = center - width;
}
else if (express == ZERO){
x = center;
}
else if (express == MORE){
x = center + width;
}
else if (express == MOREMORE){
x = center + 2.0 * width;
}
else{
printf("wrong scale expression\n");
exit(-1); // 強制終了
}
};
// Y座標の値を最大値で更新する
void func_Max(double b){
y = max(b, y);
};
// Y座標の値をリセット(y=0)する
void func_Reset(){
y = 0.0;
};
// X座標を返す
double func_X(void){
return x;
};
// (最大値で更新された、最後の)Y座標を返す
double func_Y(){
return y;
};
};
typedef enum post{
HEAD = 1, // 部長
CHIEF = 2, // 課長
STAFF = 3 // 平社員
} POST;
typedef struct person{
int absences; // 0:帰宅、 1:残業
char name[10];
int section;
int number;
POST post;
double p_x;
double p_y;
double expected_return_time; // 17.5(17:30)から、21.0(21:00)までの時間)
double going_home_ratio;
} PERSON;
const int SECTION = 6;
const int MEMBER = 8;
const int ALL_PERSON_COUNTER = 49; // 6つの課、それぞれ8人と部長
PERSON person[] = {
{1,"部長",-1,-1, HEAD, 0.0, 0.0},
{1,"社員", 0, 0, STAFF, 3.5 + 0.0 + 0.0, -4.4 + 0.0 + 0.0},
{1,"社員", 0, 1, STAFF, 3.5 + 0.0 + 1.2, -4.4 + 0.0 + 0.0},
{1,"社員", 0, 2, STAFF, 3.5 + 0.0 + 2.4, -4.4 + 0.0 + 0.0},
{1,"社員", 0, 3, STAFF, 3.5 + 0.0 + 3.6, -4.4 + 0.0 + 0.0},
{1,"課長", 0, 4, CHIEF, 3.5 + 0.0 + 0.0, -4.4 + 0.0 + 2.0},
{1,"社員", 0, 5, STAFF, 3.5 + 0.0 + 1.2, -4.4 + 0.0 + 2.0},
{1,"社員", 0, 6, STAFF, 3.5 + 0.0 + 2.4, -4.4 + 0.0 + 2.0},
{1,"社員", 0, 7, STAFF, 3.5 + 0.0 + 3.6, -4.4 + 0.0 + 2.0},
{1,"社員", 1, 0, STAFF, 3.5 + 4.8 + 0.0, -4.4 + 0.0 + 0.0},
{1,"社員", 1, 1, STAFF, 3.5 + 4.8 + 1.2, -4.4 + 0.0 + 0.0},
{1,"社員", 1, 2, STAFF, 3.5 + 4.8 + 2.4, -4.4 + 0.0 + 0.0},
{1,"課長", 1, 3, CHIEF, 3.5 + 4.8 + 3.6, -4.4 + 0.0 + 0.0},
{1,"社員", 1, 4, STAFF, 3.5 + 4.8 + 0.0, -4.4 + 0.0 + 2.0},
{1,"社員", 1, 5, STAFF, 3.5 + 4.8 + 1.2, -4.4 + 0.0 + 2.0},
{1,"社員", 1, 6, STAFF, 3.5 + 4.8 + 2.4, -4.4 + 0.0 + 2.0},
{1,"社員", 1, 7, STAFF, 3.5 + 4.8 + 3.6, -4.4 + 0.0 + 2.0},
{1,"社員", 2, 0, STAFF, 3.5 + 0.0 + 0.0, -4.4 + 3.4 + 0.0},
{1,"社員", 2, 1, STAFF, 3.5 + 0.0 + 1.2, -4.4 + 3.4 + 0.0},
{1,"課長", 2, 2, CHIEF, 3.5 + 0.0 + 2.4, -4.4 + 3.4 + 0.0},
{1,"社員", 2, 3, STAFF, 3.5 + 0.0 + 3.6, -4.4 + 3.4 + 0.0},
{1,"社員", 2, 4, STAFF, 3.5 + 0.0 + 0.0, -4.4 + 3.4 + 2.0},
{1,"社員", 2, 5, STAFF, 3.5 + 0.0 + 1.2, -4.4 + 3.4 + 2.0},
{1,"社員", 2, 6, STAFF, 3.5 + 0.0 + 2.4, -4.4 + 3.4 + 2.0},
{1,"社員", 2, 7, STAFF, 3.5 + 0.0 + 3.6, -4.4 + 3.4 + 2.0},
{1,"社員", 3, 0, STAFF, 3.5 + 4.8 + 0.0, -4.4 + 3.4 + 0.0},
{1,"社員", 3, 1, STAFF, 3.5 + 4.8 + 1.2, -4.4 + 3.4 + 0.0},
{1,"社員", 3, 2, STAFF, 3.5 + 4.8 + 2.4, -4.4 + 3.4 + 0.0},
{1,"社員", 3, 3, STAFF, 3.5 + 4.8 + 3.6, -4.4 + 3.4 + 0.0},
{1,"社員", 3, 4, STAFF, 3.5 + 4.8 + 0.0, -4.4 + 3.4 + 2.0},
{1,"社員", 3, 5, STAFF, 3.5 + 4.8 + 1.2, -4.4 + 3.4 + 2.0},
{1,"課長", 3, 6, CHIEF, 3.5 + 4.8 + 2.4, -4.4 + 3.4 + 2.0},
{1,"社員", 3, 7, STAFF, 3.5 + 4.8 + 3.6, -4.4 + 3.4 + 2.0},
{1,"課長", 4, 0, CHIEF, 3.5 + 0.0 + 0.0, -4.4 + 6.8 + 0.0},
{1,"社員", 4, 1, STAFF, 3.5 + 0.0 + 1.2, -4.4 + 6.8 + 0.0},
{1,"社員", 4, 2, STAFF, 3.5 + 0.0 + 2.4, -4.4 + 6.8 + 0.0},
{1,"社員", 4, 3, STAFF, 3.5 + 0.0 + 3.6, -4.4 + 6.8 + 0.0},
{1,"社員", 4, 4, STAFF, 3.5 + 0.0 + 0.0, -4.4 + 6.8 + 2.0},
{1,"社員", 4, 5, STAFF, 3.5 + 0.0 + 1.2, -4.4 + 6.8 + 2.0},
{1,"社員", 4, 6, STAFF, 3.5 + 0.0 + 2.4, -4.4 + 6.8 + 2.0},
{1,"社員", 4, 7, STAFF, 3.5 + 0.0 + 3.6, -4.4 + 6.8 + 2.0},
{1,"社員", 5, 0, STAFF, 3.5 + 4.8 + 0.0, -4.4 + 6.8 + 0.0},
{1,"社員", 5, 1, STAFF, 3.5 + 4.8 + 1.2, -4.4 + 6.8 + 0.0},
{1,"社員", 5, 2, STAFF, 3.5 + 4.8 + 2.4, -4.4 + 6.8 + 0.0},
{1,"社員", 5, 3, STAFF, 3.5 + 4.8 + 3.6, -4.4 + 6.8 + 0.0},
{1,"課長", 5, 4, CHIEF, 3.5 + 4.8 + 0.0, -4.4 + 6.8 + 2.0},
{1,"社員", 5, 5, STAFF, 3.5 + 4.8 + 1.2, -4.4 + 6.8 + 2.0},
{1,"社員", 5, 6, STAFF, 3.5 + 4.8 + 2.4, -4.4 + 6.8 + 2.0},
{1,"社員", 5, 7, STAFF, 3.5 + 4.8 + 3.6, -4.4 + 6.8 + 2.0},
};
double distance(PERSON* person1, PERSON* person2)
{
double d = 0.0;
d = pow((person1->p_x - person2->p_x),2.0);
d += pow((person1->p_y - person2->p_y),2.0);
d = sqrt(d);
return d;
}
int main()
{
PERSON* cheif[SECTION];
// 初期状態 (最初は個人の帰宅時間は、乱数で設定)
for (int i = 0; i < ALL_PERSON_COUNTER; i++){
// 各個人の帰宅時間を設定
person[i].expected_return_time = 17.5 + (21.0 -17.5) * rand()/ (1.0 + RAND_MAX);
// person[i].expected_return_time = 18.5;
// 各課の課長を選定(後で探すのが面倒なので)
if (person[i].post == CHIEF){
cheif[person[i].section] = &person[i];
}
}
/*
for (int i = 0; i < ALL_PERSON_COUNTER; i++){
printf("%d, expected_return_time = %f\n",i, person[i].expected_return_time);
printf("%d, expected_return_time = %d\n",i, person[i].absences);
}
*/
// 残業時間
condition_MF3 OverTime_Short(1.0, 2.0, LESS); // 1時間
condition_MF3 OverTime_Middle(1.0, 2.0, ZERO); // 2時間
condition_MF3 OverTime_Long(1.0, 2.0, MORE); // 3時間
// 部長からの距離
condition_MF3 DistanceFrom_HEAD_Near(7.0, 7.0, LESS); // 7メートル
condition_MF3 DistanceFrom_HEAD_Middle(7.0, 7.0, ZERO); // 14メートル
condition_MF3 DistanceFrom_HEAD_Far(7.0, 7.0, MORE); // 21メートル
// 課長からの距離
condition_MF3 DistanceFrom_CHIEF_Near(1.5, 1.5, LESS); // 1.5メートル
condition_MF3 DistanceFrom_CHIEF_Middle(1.5, 1.5, ZERO); // 3.0メートル
condition_MF3 DistanceFrom_CHIEF_Far(1.5, 1.5, MORE); // 4.5メートル
// 残っている課の人数
condition_MF3 Staying_COUNT_Less(2, 2, LESS); // 2人
condition_MF3 Staying_COUNT_Middle(2, 2, ZERO); // 4人
condition_MF3 Staying_COUNT_Many(2, 2, MORE); // 6人
// 帰宅度数
action_MF3 GoingHome_Hard(0.5, 0.5, LESS);// 帰宅度数 0.0 → 帰宅できない
action_MF3 GoingHome_Middle(0.5, 0.5, ZERO);// 帰宅度数 0.5 → 帰宅ボーダ
action_MF3 GoingHome_Easy(0.5, 0.5, MORE);// 帰宅度数 1.0 → 帰宅できる
double present_time =17.5;
while (present_time < 24.0){
present_time += 0.01; // 一番後ろで加算すると忘れそうなので
printf("present_time = %f\n", present_time);
// 部の中の課の全体状況の把握
if (person[0].expected_return_time < present_time){ // 部長は特別あつかい
person[0].absences = 0;
}
int staying_count[SECTION] ={}; // 各課(Section)で残っている人数を格納(最初はゼロリセット)
for (int i = 0; i < ALL_PERSON_COUNTER; i++){
if (person[i].absences == 1){
staying_count[person[i].section] += 1; // (Section)に一人追加
}
}
printf("time = %f, %d,%d,%d,%d,%d,%d\n",
present_time,
staying_count[0],
staying_count[1],
staying_count[2],
staying_count[3],
staying_count[4],
staying_count[5]);
for (int i = 1; i < ALL_PERSON_COUNTER; i++){ // person[0]の部長は神様→誰からも影響を受けない(とする)
/*
[ルール1]
全社員は、予定時間の1時間のまでの帰宅度数0.0
1時間から2時間までの帰宅度数0.5
2時間以上の帰宅度数1.0
*/
double r11 = min(OverTime_Short.func(present_time - person[i].expected_return_time));
GoingHome_Hard.func_Max(r11);
double r12 = min( OverTime_Middle.func(present_time - person[i].expected_return_time));
GoingHome_Middle.func_Max(r12);
double r13 = min(OverTime_Long.func(present_time - person[i].expected_return_time));
GoingHome_Easy.func_Max(r13);
if (person[i].post == CHIEF){ // 課長の場合の付加条件
/*
ルール2
部長が残っている場合、部長から5m以内の課長は、帰宅度数0.0
5m～10m以内の課長は、帰宅度数0.5
10m以上の課長は、帰宅度数1.0
*/
double dis = distance(&person[0], &person[i]);
double r21 = min((double)(person[0].absences), DistanceFrom_HEAD_Near.func(dis));
GoingHome_Hard.func_Max(r21);
double r22 = min((double)(person[0].absences), DistanceFrom_HEAD_Middle.func(dis));
GoingHome_Middle.func_Max(r22);
double r23 = min((double)(person[0].absences), DistanceFrom_HEAD_Far.func(dis));
GoingHome_Easy.func_Max(r23);
GoingHome_Middle.func_Max(r23);
}
if (person[i].post == STAFF){ // (ヒラ)社員の場合の付加条件
/*
ルール3
課長が残っている場合、課長から1.5m以内のメンバは、帰宅度数0.0
課長から3.0m以内のメンバは、帰宅度数0.5
課長から4.5m以内のメンバは、帰宅度数0.7
*/
double dis = distance(cheif[person[i].section], &person[i]);
double r31 = min((double)(cheif[person[i].section]->absences), DistanceFrom_CHIEF_Near.func(dis));
GoingHome_Hard.func_Max(r31);
double r32 = min((double)(cheif[person[i].section]->absences), DistanceFrom_CHIEF_Middle.func(dis));
GoingHome_Middle.func_Max(r32);
double r33 = min((double)(cheif[person[i].section]->absences), DistanceFrom_CHIEF_Far.func(dis));
GoingHome_Easy.func_Max(r33);
GoingHome_Middle.func_Max(r33);
}
#if 0
/*
ルール4
同じ課のメンバのの人数が、6人以上残っている場合は、帰宅度数0.3
4人残っている場合は、帰宅度数0.6
2人以下の場合は、帰宅度数1.0
*/
int num = staying_count[person[i].section];
double r41 = min(Staying_COUNT_Less.func((double)num));
GoingHome_Easy.func_Max(r41);
double r42 = min(Staying_COUNT_Middle.func((double)num));
GoingHome_Middle.func_Max(r42);
double r43 = min(Staying_COUNT_Many.func((double)num));
GoingHome_Hard.func_Max(r43);
#endif
/*
ルールに振れない場合は、min-max重心法の分母がゼロになり、
ゼロ割が発生する場合がある為、それを回避する
*/
double denominator = // 分母
GoingHome_Easy.func_Y() +
GoingHome_Middle.func_Y() +
GoingHome_Hard.func_Y();
double numerator = // 分子
GoingHome_Easy.func_X() * GoingHome_Easy.func_Y() +
GoingHome_Middle.func_X() * GoingHome_Middle.func_Y() +
GoingHome_Hard.func_X() * GoingHome_Hard.func_Y();
// 推論結果 (分母がゼロの場合は、推論結果は前回と同値とする)
if ( denominator != 0.0){
person[i].going_home_ratio = numerator / denominator ;
}
if (person[i].going_home_ratio > 0.5){
person[i].absences = 0; // 0:帰宅、 1:残業
}
// 後件部メンバーシップ関数のリセット(ループさせる時は必ずリセットする)
GoingHome_Easy.func_Reset();
GoingHome_Middle.func_Reset();
GoingHome_Hard.func_Reset();
}
}
for (int i = 0; i < ALL_PERSON_COUNTER; i++){
printf("%d, absences = %d\n",i, person[i].absences);
}
return 0;
}
人口シミュレーションプログラム
/////////////////////////////////////////
// gcc -g life_0813.c -o life_0813
/////////////////////////////////////////
#include <stdio.h>
#include <stdlib.h>
/// グローバル変数で強行する
struct date
{
int day;
int month;
int year;
};
enum sex {woman, man};
enum marrige {unmarried, married, divorce, remarriage};
struct person {
int age; //年齢
enum sex sex; // 性別
enum marrige marrige; // 成婚
struct person *prev; /* 前の構造体を示すポインタ */
struct person *next; /* 次の構造体を示すポインタ */
};
double men[100],women[100]; // 年齢別人口 平成22年データ 単位は1000人
double men_death_rate[100],women_death_rate[100]; //死亡率 平成22年データ
double men_unmarried_rate[100],women_unmarried_rate[100]; //未婚率 平成22年データ
double men_existance_matching_rate[100]; // 有配偶率 (平成22年)
double women_existance_matching_rate[100]; // 有配偶率 (平成22年)
// 初婚、再婚関係なく、その世代に対して。
// 結婚している比率
double men_divorce_rate[100]; // 有配偶離婚率
double women_divorce_rate[100]; // 有配偶離婚率
// ○ 結婚している人に対する離婚率
double men_remarrige_ratio[100];
double women_remarrige_ratio[100]; // 再婚率 (2010年)
int initial_data();// 死亡率 平成22年データ (資料 厚生労働省大臣官房統計情報部人口動態・保健統計課「人口動態統計」)
void delete_person(
struct person **p_person,
struct person **p_first_person,
struct person **p_last_person) // メモリを消す処理e
{
struct person *temp_p_person;
if (*p_first_person == *p_last_person){
//printf("p_first_person == p_last_person\n");
exit(0);
}
if (*p_person == *p_first_person){ // 最初の場合
*p_first_person = (*p_person)->next;
(*p_first_person)->prev = NULL;
//printf("C1");
free(*p_person);
*p_person = *p_first_person;
}
else if (*p_person == *p_last_person){ //最後の場合
*p_last_person = (*p_person)->prev;
(*p_last_person)->next = NULL;
//printf("C2");
free(*p_person);
*p_person = *p_last_person;
}
else {
(*p_person)->next->prev = (*p_person)->prev;
(*p_person)->prev->next = (*p_person)->next;
temp_p_person = (*p_person)->prev; // 一つ前のポインタに退避
//printf("C3");
free(*p_person);
*p_person = temp_p_person;
}
}
population_counter(struct person *p_first_person)
{
struct person *p_person;
int count;
p_person = p_first_person; //最初の一人
count = 0;
while (p_person != NULL){
count++;
p_person = p_person->next;
}
//printf("count=%d \n", count);
}
int main ()
{
int i, k, count;
struct person *p_person, *p_prev_person, *p_next_person;
struct person *p_first_person, *p_last_person;
int women_pop, men_pop;
double dd;
// 日本国民一億人のデータを作る
//printf("checked -1.\r\n");
initial_data(); // 初期データ入力
//printf("checked 0.\r\n");
srand(10); // 乱数のシード
//////////// 現状データの入力 ////////////
// 最初の一人(0人目) 99歳の女性と仮定する。
p_person= (struct person *)malloc(sizeof(struct person));
if(p_person == NULL) {
printf("メモリが確保できません\n");
exit(EXIT_FAILURE);
}
p_person->sex = woman;
p_person->age = 99;
p_first_person = p_person; //最初の一人
// (最後に)ポインタをリンクする
p_person->prev = NULL;
p_prev_person = p_person;
for(i=99; i>=0; i--){
women_pop = women[i] * 1000;
men_pop = men[i] * 1000;
for(k=0; k<women_pop; k++){
p_person= (struct person *)malloc(sizeof(struct person));
if(p_person == NULL) {
printf("メモリが確保できません %d\n",i);
exit(EXIT_FAILURE);
}
p_person->sex = woman;
p_person->age = i;
// (最後に)ポインタをリンクする
p_prev_person->next = p_person;
p_person->prev = p_prev_person;
p_person->next = NULL;
p_prev_person = p_person;
}
for(k=0; k<men_pop; k++){
p_person= (struct person *)malloc(sizeof(struct person));
if(p_person == NULL) {
printf("メモリが確保できません %d\n",i);
exit(EXIT_FAILURE);
}
p_person->sex = man;
p_person->age = i;
// (最後に)ポインタをリンクする
p_prev_person->next = p_person;
p_person->prev = p_prev_person;
p_person->next = NULL;
p_prev_person = p_person;
}
}
p_last_person = p_person;
//printf("checked 1.\n");
// 既婚(離婚も含む)・未婚の入力 (乱数で入力する) (離婚人口も含む)
p_person = p_first_person; //最初の一人
while (p_person != NULL){
if (p_person->sex == woman){ // 女性の場合
if (women_unmarried_rate[p_person->age] >= rand()/32768.0){
p_person->marrige = unmarried;
}
else {
p_person->marrige = married;
}
}
else{// 男性の場合
if (men_unmarried_rate[p_person->age] >= rand()/32768.0){
p_person->marrige = unmarried;
}
else {
p_person->marrige = married;
}
}
p_person = p_person->next;
}
//printf("count=%d \n", count);
// 離婚させる (ここは男女で分ける必要ないが、詳細データが手に入った時に反映)
p_person = p_first_person; //最初の一人
while (p_person != NULL){
if (p_person->sex == woman){ // 女性の場合
if ((p_person->marrige == married) || (p_person->marrige == remarriage)){ // 結婚している
if( women_divorce_rate[p_person->age] >= rand()/32768.0){
p_person->marrige = divorce; // 離婚させる
}
}
}
else{// 男性の場合
if ((p_person->marrige == married) || (p_person->marrige == remarriage)){ // 結婚している
if( men_divorce_rate[p_person->age] >= rand()/32768.0){
p_person->marrige = divorce; // 離婚させる
}
}
}
p_person = p_person->next;
}
// 再婚させる
// 離婚している人に対する再婚率 (に変換する式)
// =
// 再婚率 x その世代の人口数 / 離婚(×未婚、結婚)人口
// としなければなない
// 上記の解釈間違いの可能性あり
// 離婚した人を再婚させる (ここは男女で分ける必要がある)
p_person = p_first_person; //最初の一人
while (p_person != NULL){
if (p_person->sex == woman){ // 女性の場合
if (p_person->marrige == divorce){ // 離婚している
if( women_remarrige_ratio[p_person->age] >= rand()/32768.0){
p_person->marrige = remarriage; // 再婚させる
}
}
}
else{// 男性の場合
if (p_person->marrige == divorce){ // 離婚している
if( men_remarrige_ratio[p_person->age] >= rand()/32768.0){
p_person->marrige = remarriage; // 再婚させる
}
}
}
p_person = p_person->next;
}
// (T.B.D.)
//初期値チェックルーチン
#if 1
p_person = p_first_person; //最初の一人
printf("性別,年齢,成婚\n");
while (p_person != NULL){
if (p_person->sex == woman)
printf("女性,");
else
printf("男性,");
printf("%d,",p_person->age);
if (p_person->marrige == unmarried)
printf("未婚\n");
else if (p_person->marrige == married)
printf("結婚\n");
else if (p_person->marrige == divorce)
printf("離婚\n");
else if (p_person->marrige == remarriage)
printf("再婚\n");
p_person = p_person->next;
}
//printf("count=%d \n", count);
#endif
//初期値チェックルーチン 終り
//////////// 現状データの入力 終わり ////////////
p_person = p_first_person; //最初の一人
count = 0;
while (p_person != NULL){
count++;
p_person = p_person->next;
}
//printf("count=%d \n", count);
// (1)100歳以上は、いない(死んだ)ことにする。
// → person->age が100になったらオブジェクトを開放
#if 0
//時間計測の為、コメントアウト
for(i=0; i<100; i++){//100年分を回す
//printf("%d\n",i);
p_person = p_first_person; //最初の一人
while (p_person != NULL){
p_person->age++;
if (p_person->age == 100){ //100歳以上は削除
delete_person(&p_person,&p_first_person,&p_last_person);
}
p_person = p_person->next;
}
population_counter(p_first_person);
}
p_person = p_first_person; //最初の一人
count = 0;
while (p_person != NULL){
count++;
p_person = p_person->next;
}
//printf("count=%d \n", count);
#endif // 時間計測の為、コメントアウト
}
int initial_data()
{
men[ 0]=549 ; men_death_rate[ 0]=0.0025 ;
men[ 1]=535 ; men_death_rate[ 1]=0.0004 ;
men[ 2]=535 ; men_death_rate[ 2]=0.0002 ;
men[ 3]=550 ; men_death_rate[ 3]=0.0002 ;
men[ 4]=548 ; men_death_rate[ 4]=0.0002 ;
men[ 5]=544 ; men_death_rate[ 5]=0.0001 ;
men[ 6]=542 ; men_death_rate[ 6]=0.0001 ;
men[ 7]=562 ; men_death_rate[ 7]=0.0001 ;
men[ 8]=574 ; men_death_rate[ 8]=0.0001 ;
men[ 9]=589 ; men_death_rate[ 9]=0.0001 ;
men[10]=597 ; men_death_rate[10]=0.0001 ;
men[11]=604 ; men_death_rate[11]=0.0001 ;
men[12]=604 ; men_death_rate[12]=0.0001 ;
men[13]=613 ; men_death_rate[13]=0.0001 ;
men[14]=610 ; men_death_rate[14]=0.0001 ;
men[15]=607 ; men_death_rate[15]=0.0003 ;
men[16]=627 ; men_death_rate[16]=0.0003 ;
men[17]=632 ; men_death_rate[17]=0.0003 ;
men[18]=621 ; men_death_rate[18]=0.0003 ;
men[19]=631 ; men_death_rate[19]=0.0003 ;
men[20]=623 ; men_death_rate[20]=0.0006 ;
men[21]=632 ; men_death_rate[21]=0.0006 ;
men[22]=648 ; men_death_rate[22]=0.0006 ;
men[23]=668 ; men_death_rate[23]=0.0006 ;
men[24]=683 ; men_death_rate[24]=0.0006 ;
men[25]=697 ; men_death_rate[25]=0.0007 ;
men[26]=723 ; men_death_rate[26]=0.0007 ;
men[27]=745 ; men_death_rate[27]=0.0007 ;
men[28]=754 ; men_death_rate[28]=0.0007 ;
men[29]=754 ; men_death_rate[29]=0.0007 ;
men[30]=764 ; men_death_rate[30]=0.0008 ;
men[31]=797 ; men_death_rate[31]=0.0008 ;
men[32]=818 ; men_death_rate[32]=0.0008 ;
men[33]=852 ; men_death_rate[33]=0.0008 ;
men[34]=873 ; men_death_rate[34]=0.0008 ;
men[35]=917 ; men_death_rate[35]=0.0010 ;
men[36]=960 ; men_death_rate[36]=0.0010 ;
men[37]=1012 ; men_death_rate[37]=0.0010 ;
men[38]=1028 ; men_death_rate[38]=0.0010 ;
men[39]=1010 ; men_death_rate[39]=0.0010 ;
men[40]=982 ; men_death_rate[40]=0.0015 ;
men[41]=954 ; men_death_rate[41]=0.0015 ;
men[42]=937 ; men_death_rate[42]=0.0015 ;
men[43]=916 ; men_death_rate[43]=0.0015 ;
men[44]=915 ; men_death_rate[44]=0.0015 ;
men[45]=713 ; men_death_rate[45]=0.0024 ;
men[46]=882 ; men_death_rate[46]=0.0024 ;
men[47]=826 ; men_death_rate[47]=0.0024 ;
men[48]=805 ; men_death_rate[48]=0.0024 ;
men[49]=778 ; men_death_rate[49]=0.0024 ;
men[50]=765 ; men_death_rate[50]=0.0038 ;
men[51]=770 ; men_death_rate[51]=0.0038 ;
men[52]=783 ; men_death_rate[52]=0.0038 ;
men[53]=761 ; men_death_rate[53]=0.0038 ;
men[54]=740 ; men_death_rate[54]=0.0038 ;
men[55]=776 ; men_death_rate[55]=0.0063 ;
men[56]=803 ; men_death_rate[56]=0.0063 ;
men[57]=803 ; men_death_rate[57]=0.0063 ;
men[58]=850 ; men_death_rate[58]=0.0063 ;
men[59]=896 ; men_death_rate[59]=0.0063 ;
men[60]=949 ; men_death_rate[60]=0.0093 ;
men[61]=1018 ; men_death_rate[61]=0.0093 ;
men[62]=1111 ; men_death_rate[62]=0.0093 ;
men[63]=1099 ; men_death_rate[63]=0.0093 ;
men[64]=1042 ; men_death_rate[64]=0.0093 ;
men[65]=645 ; men_death_rate[65]=0.0146 ;
men[66]=684 ; men_death_rate[66]=0.0146 ;
men[67]=825 ; men_death_rate[67]=0.0146 ;
men[68]=794 ; men_death_rate[68]=0.0146 ;
men[69]=809 ; men_death_rate[69]=0.0146 ;
men[70]=780 ; men_death_rate[70]=0.0227 ;
men[71]=698 ; men_death_rate[71]=0.0227 ;
men[72]=599 ; men_death_rate[72]=0.0227 ;
men[73]=627 ; men_death_rate[73]=0.0227 ;
men[74]=631 ; men_death_rate[74]=0.0227 ;
men[75]=616 ; men_death_rate[75]=0.0396 ;
men[76]=571 ; men_death_rate[76]=0.0396 ;
men[77]=521 ; men_death_rate[77]=0.0396 ;
men[78]=501 ; men_death_rate[78]=0.0396 ;
men[79]=470 ; men_death_rate[79]=0.0396 ;
men[80]=430 ; men_death_rate[80]=0.0705 ;
men[81]=385 ; men_death_rate[81]=0.0705 ;
men[82]=350 ; men_death_rate[82]=0.0705 ;
men[83]=316 ; men_death_rate[83]=0.0705 ;
men[84]=281 ; men_death_rate[84]=0.0705 ;
men[85]=247 ; men_death_rate[85]=0.1200 ;
men[86]=202 ; men_death_rate[86]=0.1200 ;
men[87]=158 ; men_death_rate[87]=0.1200 ;
men[88]=122 ; men_death_rate[88]=0.1200 ;
men[89]=98 ; men_death_rate[89]=0.1200 ;
men[90]=78 ; men_death_rate[90]=0.2025 ;
men[91]=67 ; men_death_rate[91]=0.2025 ;
men[92]=44 ; men_death_rate[92]=0.2025 ;
men[93]=36 ; men_death_rate[93]=0.2025 ;
men[94]=28 ; men_death_rate[94]=0.2025 ;
men[95]=21 ; men_death_rate[95]=0.3188 ;
men[96]=15 ; men_death_rate[96]=0.3188 ;
men[97]=11 ; men_death_rate[97]=0.3188 ;
men[98]=7 ; men_death_rate[98]=0.3188 ;
men[99]=5 ; men_death_rate[99]=0.3188 ;
women[ 0]=520; women_death_rate[ 0]=0.0021 ;
women[ 1]=510; women_death_rate[ 1]=0.0004 ;
women[ 2]=511; women_death_rate[ 2]=0.0002 ;
women[ 3]=525; women_death_rate[ 3]=0.0001 ;
women[ 4]=522; women_death_rate[ 4]=0.0001 ;
women[ 5]=518; women_death_rate[ 5]=0.0001 ;
women[ 6]=517; women_death_rate[ 6]=0.0001 ;
women[ 7]=538; women_death_rate[ 7]=0.0001 ;
women[ 8]=545; women_death_rate[ 8]=0.0001 ;
women[ 9]=561; women_death_rate[ 9]=0.0001 ;
women[10]=568; women_death_rate[10]=0.0001 ;
women[11]=573; women_death_rate[11]=0.0001 ;
women[12]=576; women_death_rate[12]=0.0001 ;
women[13]=585; women_death_rate[13]=0.0001 ;
women[14]=583; women_death_rate[14]=0.0001 ;
women[15]=578; women_death_rate[15]=0.0002 ;
women[16]=595; women_death_rate[16]=0.0002 ;
women[17]=597; women_death_rate[17]=0.0002 ;
women[18]=589; women_death_rate[18]=0.0002 ;
women[19]=599; women_death_rate[19]=0.0002 ;
women[20]=596; women_death_rate[20]=0.0003 ;
women[21]=605; women_death_rate[21]=0.0003 ;
women[22]=622; women_death_rate[22]=0.0003 ;
women[23]=638; women_death_rate[23]=0.0003 ;
women[24]=655; women_death_rate[24]=0.0003 ;
women[25]=667; women_death_rate[25]=0.0003 ;
women[26]=697; women_death_rate[26]=0.0003 ;
women[27]=719; women_death_rate[27]=0.0003 ;
women[28]=729; women_death_rate[28]=0.0003 ;
women[29]=734; women_death_rate[29]=0.0003 ;
women[30]=742; women_death_rate[30]=0.0004 ;
women[31]=774; women_death_rate[31]=0.0004 ;
women[32]=794; women_death_rate[32]=0.0004 ;
women[33]=828; women_death_rate[33]=0.0004 ;
women[34]=849; women_death_rate[34]=0.0004 ;
women[35]=890; women_death_rate[35]=0.0006 ;
women[36]=931; women_death_rate[36]=0.0006 ;
women[37]=982; women_death_rate[37]=0.0006 ;
women[38]=1001; women_death_rate[38]=0.0006 ;
women[39]=981; women_death_rate[39]=0.0006 ;
women[40]=958; women_death_rate[40]=0.0008 ;
women[41]=931; women_death_rate[41]=0.0008 ;
women[42]=920; women_death_rate[42]=0.0008 ;
women[43]=902; women_death_rate[43]=0.0008 ;
women[44]=898; women_death_rate[44]=0.0008 ;
women[45]=705; women_death_rate[45]=0.0013 ;
women[46]=872; women_death_rate[46]=0.0013 ;
women[47]=815; women_death_rate[47]=0.0013 ;
women[48]=798; women_death_rate[48]=0.0013 ;
women[49]=772; women_death_rate[49]=0.0013 ;
women[50]=760; women_death_rate[50]=0.0019 ;
women[51]=768; women_death_rate[51]=0.0019 ;
women[52]=783; women_death_rate[52]=0.0019 ;
women[53]=765; women_death_rate[53]=0.0019 ;
women[54]=744; women_death_rate[54]=0.0019 ;
women[55]=783; women_death_rate[55]=0.0028 ;
women[56]=810; women_death_rate[56]=0.0028 ;
women[57]=813; women_death_rate[57]=0.0028 ;
women[58]=868; women_death_rate[58]=0.0028 ;
women[59]=918; women_death_rate[59]=0.0028 ;
women[60]=975; women_death_rate[60]=0.0039 ;
women[61]=1051; women_death_rate[61]=0.0039 ;
women[62]=1152; women_death_rate[62]=0.0039 ;
women[63]=1146; women_death_rate[63]=0.0039 ;
women[64]=1090; women_death_rate[64]=0.0039 ;
women[65]=685; women_death_rate[65]=0.0060 ;
women[66]=741; women_death_rate[66]=0.0060 ;
women[67]=903; women_death_rate[67]=0.0060 ;
women[68]=875; women_death_rate[68]=0.0060 ;
women[69]=899; women_death_rate[69]=0.0060 ;
women[70]=873; women_death_rate[70]=0.0098 ;
women[71]=793; women_death_rate[71]=0.0098 ;
women[72]=690; women_death_rate[72]=0.0098 ;
women[73]=738; women_death_rate[73]=0.0098 ;
women[74]=755; women_death_rate[74]=0.0098 ;
women[75]=753; women_death_rate[75]=0.0179 ;
women[76]=718; women_death_rate[76]=0.0179 ;
women[77]=675; women_death_rate[77]=0.0179 ;
women[78]=671; women_death_rate[78]=0.0179 ;
women[79]=646; women_death_rate[79]=0.0179 ;
women[80]=614; women_death_rate[80]=0.0343 ;
women[81]=573; women_death_rate[81]=0.0343 ;
women[82]=547; women_death_rate[82]=0.0343 ;
women[83]=515; women_death_rate[83]=0.0343 ;
women[84]=482; women_death_rate[84]=0.0343 ;
women[85]=454; women_death_rate[85]=0.0691 ;
women[86]=405; women_death_rate[86]=0.0691 ;
women[87]=349; women_death_rate[87]=0.0691 ;
women[88]=313; women_death_rate[88]=0.0691 ;
women[89]=276; women_death_rate[89]=0.0691 ;
women[90]=236; women_death_rate[90]=0.1312 ;
women[91]=213; women_death_rate[91]=0.1312 ;
women[92]=146; women_death_rate[92]=0.1312 ;
women[93]=128; women_death_rate[93]=0.1312 ;
women[94]=106; women_death_rate[94]=0.1312 ;
women[95]=87 ; women_death_rate[95]=0.2381 ;
women[96]=63 ; women_death_rate[96]=0.2381 ;
women[97]=49 ; women_death_rate[97]=0.2381 ;
women[98]=35 ; women_death_rate[98]=0.2381 ;
women[99]=25 ; women_death_rate[99]=0.2381 ;
/////////////////////////////////////////////
// 未婚率 (平成22年)
men_unmarried_rate[ 0]=1.000 ;
men_unmarried_rate[ 1]=1.000 ;
men_unmarried_rate[ 2]=1.000 ;
men_unmarried_rate[ 3]=1.000 ;
men_unmarried_rate[ 4]=1.000 ;
men_unmarried_rate[ 5]=1.000 ;
men_unmarried_rate[ 6]=1.000 ;
men_unmarried_rate[ 7]=1.000 ;
men_unmarried_rate[ 8]=1.000 ;
men_unmarried_rate[ 9]=1.000 ;
men_unmarried_rate[10]=1.000 ;
men_unmarried_rate[11]=1.000 ;
men_unmarried_rate[12]=1.000 ;
men_unmarried_rate[13]=1.000 ;
men_unmarried_rate[14]=1.000 ;
men_unmarried_rate[15]=1.000 ;
men_unmarried_rate[16]=1.000 ;
men_unmarried_rate[17]=1.000 ;
men_unmarried_rate[18]=0.975 ;
men_unmarried_rate[19]=0.975 ;
men_unmarried_rate[20]=0.910 ;
men_unmarried_rate[21]=0.910 ;
men_unmarried_rate[22]=0.910 ;
men_unmarried_rate[23]=0.910 ;
men_unmarried_rate[24]=0.910 ;
men_unmarried_rate[25]=0.645 ;
men_unmarried_rate[26]=0.645 ;
men_unmarried_rate[27]=0.645 ;
men_unmarried_rate[28]=0.645 ;
men_unmarried_rate[29]=0.645 ;
men_unmarried_rate[30]=0.413 ;
men_unmarried_rate[31]=0.413 ;
men_unmarried_rate[32]=0.413 ;
men_unmarried_rate[33]=0.413 ;
men_unmarried_rate[34]=0.413 ;
men_unmarried_rate[35]=0.370 ;
men_unmarried_rate[36]=0.370 ;
men_unmarried_rate[37]=0.370 ;
men_unmarried_rate[38]=0.370 ;
men_unmarried_rate[39]=0.370 ;
men_unmarried_rate[40]=0.229 ;
men_unmarried_rate[41]=0.229 ;
men_unmarried_rate[42]=0.229 ;
men_unmarried_rate[43]=0.229 ;
men_unmarried_rate[44]=0.229 ;
men_unmarried_rate[45]=0.166 ;
men_unmarried_rate[46]=0.166 ;
men_unmarried_rate[47]=0.166 ;
men_unmarried_rate[48]=0.166 ;
men_unmarried_rate[49]=0.166 ;
men_unmarried_rate[50]=0.189 ;
men_unmarried_rate[51]=0.189 ;
men_unmarried_rate[52]=0.189 ;
men_unmarried_rate[53]=0.189 ;
men_unmarried_rate[54]=0.189 ;
men_unmarried_rate[55]=0.139 ;
men_unmarried_rate[56]=0.139 ;
men_unmarried_rate[57]=0.139 ;
men_unmarried_rate[58]=0.139 ;
men_unmarried_rate[59]=0.139 ;
men_unmarried_rate[60]=0.068 ;
men_unmarried_rate[61]=0.068 ;
men_unmarried_rate[62]=0.068 ;
men_unmarried_rate[63]=0.068 ;
men_unmarried_rate[64]=0.068 ;
men_unmarried_rate[65]=0.042 ;
men_unmarried_rate[66]=0.042 ;
men_unmarried_rate[67]=0.042 ;
men_unmarried_rate[68]=0.042 ;
men_unmarried_rate[69]=0.042 ;
men_unmarried_rate[70]=0.015 ;
men_unmarried_rate[71]=0.015 ;
men_unmarried_rate[72]=0.015 ;
men_unmarried_rate[73]=0.015 ;
men_unmarried_rate[74]=0.015 ;
men_unmarried_rate[75]=0.012 ;
men_unmarried_rate[76]=0.012 ;
men_unmarried_rate[77]=0.012 ;
men_unmarried_rate[78]=0.012 ;
men_unmarried_rate[79]=0.012 ;
men_unmarried_rate[80]=0.012 ;
men_unmarried_rate[81]=0.012 ;
men_unmarried_rate[82]=0.012 ;
men_unmarried_rate[83]=0.012 ;
men_unmarried_rate[84]=0.012 ;
men_unmarried_rate[85]=0.011 ;
men_unmarried_rate[86]=0.011 ;
men_unmarried_rate[87]=0.011 ;
men_unmarried_rate[88]=0.011 ;
men_unmarried_rate[89]=0.011 ;
men_unmarried_rate[90]=0.011 ;
men_unmarried_rate[91]=0.011 ;
men_unmarried_rate[92]=0.011 ;
men_unmarried_rate[93]=0.011 ;
men_unmarried_rate[94]=0.011 ;
men_unmarried_rate[95]=0.011 ;
men_unmarried_rate[96]=0.011 ;
men_unmarried_rate[97]=0.011 ;
men_unmarried_rate[98]=0.011 ;
men_unmarried_rate[99]=0.011 ;
women_unmarried_rate[ 0]=1.000 ;
women_unmarried_rate[ 1]=1.000 ;
women_unmarried_rate[ 2]=1.000 ;
women_unmarried_rate[ 3]=1.000 ;
women_unmarried_rate[ 4]=1.000 ;
women_unmarried_rate[ 5]=1.000 ;
women_unmarried_rate[ 6]=1.000 ;
women_unmarried_rate[ 7]=1.000 ;
women_unmarried_rate[ 8]=1.000 ;
women_unmarried_rate[ 9]=1.000 ;
women_unmarried_rate[10]=1.000 ;
women_unmarried_rate[11]=1.000 ;
women_unmarried_rate[12]=1.000 ;
women_unmarried_rate[13]=1.000 ;
women_unmarried_rate[14]=1.000 ;
women_unmarried_rate[15]=1.000 ;
women_unmarried_rate[16]=0.984 ;
women_unmarried_rate[17]=0.984 ;
women_unmarried_rate[18]=0.984 ;
women_unmarried_rate[19]=0.984 ;
women_unmarried_rate[20]=0.898 ;
women_unmarried_rate[21]=0.898 ;
women_unmarried_rate[22]=0.898 ;
women_unmarried_rate[23]=0.898 ;
women_unmarried_rate[24]=0.898 ;
women_unmarried_rate[25]=0.607 ;
women_unmarried_rate[26]=0.607 ;
women_unmarried_rate[27]=0.607 ;
women_unmarried_rate[28]=0.607 ;
women_unmarried_rate[29]=0.607 ;
women_unmarried_rate[30]=0.374 ;
women_unmarried_rate[31]=0.374 ;
women_unmarried_rate[32]=0.374 ;
women_unmarried_rate[33]=0.374 ;
women_unmarried_rate[34]=0.374 ;
women_unmarried_rate[35]=0.250 ;
women_unmarried_rate[36]=0.250 ;
women_unmarried_rate[37]=0.250 ;
women_unmarried_rate[38]=0.250 ;
women_unmarried_rate[39]=0.250 ;
women_unmarried_rate[40]=0.224 ;
women_unmarried_rate[41]=0.224 ;
women_unmarried_rate[42]=0.224 ;
women_unmarried_rate[43]=0.224 ;
women_unmarried_rate[44]=0.224 ;
women_unmarried_rate[45]=0.154 ;
women_unmarried_rate[46]=0.154 ;
women_unmarried_rate[47]=0.154 ;
women_unmarried_rate[48]=0.154 ;
women_unmarried_rate[49]=0.154 ;
women_unmarried_rate[50]=0.114 ;
women_unmarried_rate[51]=0.114 ;
women_unmarried_rate[52]=0.114 ;
women_unmarried_rate[53]=0.114 ;
women_unmarried_rate[54]=0.114 ;
women_unmarried_rate[55]=0.073 ;
women_unmarried_rate[56]=0.073 ;
women_unmarried_rate[57]=0.073 ;
women_unmarried_rate[58]=0.073 ;
women_unmarried_rate[59]=0.073 ;
women_unmarried_rate[60]=0.055 ;
women_unmarried_rate[61]=0.055 ;
women_unmarried_rate[62]=0.055 ;
women_unmarried_rate[63]=0.055 ;
women_unmarried_rate[64]=0.055 ;
women_unmarried_rate[65]=0.053 ;
women_unmarried_rate[66]=0.053 ;
women_unmarried_rate[67]=0.053 ;
women_unmarried_rate[68]=0.053 ;
women_unmarried_rate[69]=0.053 ;
women_unmarried_rate[70]=0.033 ;
women_unmarried_rate[71]=0.033 ;
women_unmarried_rate[72]=0.033 ;
women_unmarried_rate[73]=0.033 ;
women_unmarried_rate[74]=0.033 ;
women_unmarried_rate[75]=0.044 ;
women_unmarried_rate[76]=0.044 ;
women_unmarried_rate[77]=0.044 ;
women_unmarried_rate[78]=0.044 ;
women_unmarried_rate[79]=0.044 ;
women_unmarried_rate[80]=0.078 ;
women_unmarried_rate[81]=0.078 ;
women_unmarried_rate[82]=0.078 ;
women_unmarried_rate[83]=0.078 ;
women_unmarried_rate[84]=0.078 ;
women_unmarried_rate[85]=0.033 ;
women_unmarried_rate[86]=0.033 ;
women_unmarried_rate[87]=0.033 ;
women_unmarried_rate[88]=0.033 ;
women_unmarried_rate[89]=0.033 ;
women_unmarried_rate[90]=0.033 ;
women_unmarried_rate[91]=0.033 ;
women_unmarried_rate[92]=0.033 ;
women_unmarried_rate[93]=0.033 ;
women_unmarried_rate[94]=0.033 ;
women_unmarried_rate[95]=0.033 ;
women_unmarried_rate[96]=0.033 ;
women_unmarried_rate[97]=0.033 ;
women_unmarried_rate[98]=0.033 ;
women_unmarried_rate[99]=0.033 ;
// 有配偶率 (平成22年)
// 初婚、再婚関係なく、その世代に対して。
// 結婚している比率
//国勢調査の男女別の有配偶者の数はなぜ違うのか
men_existance_matching_rate[ 0]=0.000 ;
men_existance_matching_rate[ 1]=0.000 ;
men_existance_matching_rate[ 2]=0.000 ;
men_existance_matching_rate[ 3]=0.000 ;
men_existance_matching_rate[ 4]=0.000 ;
men_existance_matching_rate[ 5]=0.000 ;
men_existance_matching_rate[ 6]=0.000 ;
men_existance_matching_rate[ 7]=0.000 ;
men_existance_matching_rate[ 8]=0.000 ;
men_existance_matching_rate[ 9]=0.000 ;
men_existance_matching_rate[10]=0.000 ;
men_existance_matching_rate[11]=0.000 ;
men_existance_matching_rate[12]=0.000 ;
men_existance_matching_rate[13]=0.000 ;
men_existance_matching_rate[14]=0.000 ;
men_existance_matching_rate[15]=0.011 ;
men_existance_matching_rate[16]=0.011 ;
men_existance_matching_rate[17]=0.011 ;
men_existance_matching_rate[18]=0.011 ;
men_existance_matching_rate[19]=0.011 ;
men_existance_matching_rate[20]=0.036 ;
men_existance_matching_rate[21]=0.036 ;
men_existance_matching_rate[22]=0.036 ;
men_existance_matching_rate[23]=0.036 ;
men_existance_matching_rate[24]=0.036 ;
men_existance_matching_rate[25]=0.241 ;
men_existance_matching_rate[26]=0.241 ;
men_existance_matching_rate[27]=0.241 ;
men_existance_matching_rate[28]=0.241 ;
men_existance_matching_rate[29]=0.241 ;
men_existance_matching_rate[30]=0.497 ;
men_existance_matching_rate[31]=0.497 ;
men_existance_matching_rate[32]=0.497 ;
men_existance_matching_rate[33]=0.497 ;
men_existance_matching_rate[34]=0.497 ;
men_existance_matching_rate[35]=0.530 ;
men_existance_matching_rate[36]=0.530 ;
men_existance_matching_rate[37]=0.530 ;
men_existance_matching_rate[38]=0.530 ;
men_existance_matching_rate[39]=0.530 ;
men_existance_matching_rate[40]=0.694 ;
men_existance_matching_rate[41]=0.694 ;
men_existance_matching_rate[42]=0.694 ;
men_existance_matching_rate[43]=0.694 ;
men_existance_matching_rate[44]=0.694 ;
men_existance_matching_rate[45]=0.741 ;
men_existance_matching_rate[46]=0.741 ;
men_existance_matching_rate[47]=0.741 ;
men_existance_matching_rate[48]=0.741 ;
men_existance_matching_rate[49]=0.741 ;
men_existance_matching_rate[50]=0.700 ;
men_existance_matching_rate[51]=0.700 ;
men_existance_matching_rate[52]=0.700 ;
men_existance_matching_rate[53]=0.700 ;
men_existance_matching_rate[54]=0.700 ;
men_existance_matching_rate[55]=0.765 ;
men_existance_matching_rate[56]=0.765 ;
men_existance_matching_rate[57]=0.765 ;
men_existance_matching_rate[58]=0.765 ;
men_existance_matching_rate[59]=0.765 ;
men_existance_matching_rate[60]=0.842 ;
men_existance_matching_rate[61]=0.842 ;
men_existance_matching_rate[62]=0.842 ;
men_existance_matching_rate[63]=0.842 ;
men_existance_matching_rate[64]=0.842 ;
men_existance_matching_rate[65]=0.824 ;
men_existance_matching_rate[66]=0.824 ;
men_existance_matching_rate[67]=0.824 ;
men_existance_matching_rate[68]=0.824 ;
men_existance_matching_rate[69]=0.824 ;
men_existance_matching_rate[70]=0.838 ;
men_existance_matching_rate[71]=0.838 ;
men_existance_matching_rate[72]=0.838 ;
men_existance_matching_rate[73]=0.838 ;
men_existance_matching_rate[74]=0.838 ;
men_existance_matching_rate[75]=0.817 ;
men_existance_matching_rate[76]=0.817 ;
men_existance_matching_rate[77]=0.817 ;
men_existance_matching_rate[78]=0.817 ;
men_existance_matching_rate[79]=0.817 ;
men_existance_matching_rate[80]=0.730 ;
men_existance_matching_rate[81]=0.730 ;
men_existance_matching_rate[82]=0.730 ;
men_existance_matching_rate[83]=0.730 ;
men_existance_matching_rate[84]=0.730 ;
men_existance_matching_rate[85]=0.708 ;
men_existance_matching_rate[86]=0.708 ;
men_existance_matching_rate[87]=0.708 ;
men_existance_matching_rate[88]=0.708 ;
men_existance_matching_rate[89]=0.708 ;
men_existance_matching_rate[90]=0.708 ;
men_existance_matching_rate[91]=0.708 ;
men_existance_matching_rate[92]=0.708 ;
men_existance_matching_rate[93]=0.708 ;
men_existance_matching_rate[94]=0.708 ;
men_existance_matching_rate[95]=0.708 ;
men_existance_matching_rate[96]=0.708 ;
men_existance_matching_rate[97]=0.708 ;
men_existance_matching_rate[98]=0.708 ;
men_existance_matching_rate[99]=0.708 ;
women_existance_matching_rate[ 0]=0.000 ;
women_existance_matching_rate[ 1]=0.000 ;
women_existance_matching_rate[ 2]=0.000 ;
women_existance_matching_rate[ 3]=0.000 ;
women_existance_matching_rate[ 4]=0.000 ;
women_existance_matching_rate[ 5]=0.000 ;
women_existance_matching_rate[ 6]=0.000 ;
women_existance_matching_rate[ 7]=0.000 ;
women_existance_matching_rate[ 8]=0.000 ;
women_existance_matching_rate[ 9]=0.000 ;
women_existance_matching_rate[10]=0.000 ;
women_existance_matching_rate[11]=0.000 ;
women_existance_matching_rate[12]=0.000 ;
women_existance_matching_rate[13]=0.000 ;
women_existance_matching_rate[14]=0.000 ;
women_existance_matching_rate[15]=0.008 ;
women_existance_matching_rate[16]=0.008 ;
women_existance_matching_rate[17]=0.008 ;
women_existance_matching_rate[18]=0.008 ;
women_existance_matching_rate[19]=0.008 ;
women_existance_matching_rate[20]=0.052 ;
women_existance_matching_rate[21]=0.052 ;
women_existance_matching_rate[22]=0.052 ;
women_existance_matching_rate[23]=0.052 ;
women_existance_matching_rate[24]=0.052 ;
women_existance_matching_rate[25]=0.312 ;
women_existance_matching_rate[26]=0.312 ;
women_existance_matching_rate[27]=0.312 ;
women_existance_matching_rate[28]=0.312 ;
women_existance_matching_rate[29]=0.312 ;
women_existance_matching_rate[30]=0.569 ;
women_existance_matching_rate[31]=0.569 ;
women_existance_matching_rate[32]=0.569 ;
women_existance_matching_rate[33]=0.569 ;
women_existance_matching_rate[34]=0.569 ;
women_existance_matching_rate[35]=0.667 ;
women_existance_matching_rate[36]=0.667 ;
women_existance_matching_rate[37]=0.667 ;
women_existance_matching_rate[38]=0.667 ;
women_existance_matching_rate[39]=0.667 ;
women_existance_matching_rate[40]=0.229 ;
women_existance_matching_rate[41]=0.229 ;
women_existance_matching_rate[42]=0.229 ;
women_existance_matching_rate[43]=0.229 ;
women_existance_matching_rate[44]=0.229 ;
women_existance_matching_rate[45]=0.712 ;
women_existance_matching_rate[46]=0.712 ;
women_existance_matching_rate[47]=0.712 ;
women_existance_matching_rate[48]=0.712 ;
women_existance_matching_rate[49]=0.712 ;
women_existance_matching_rate[50]=0.755 ;
women_existance_matching_rate[51]=0.755 ;
women_existance_matching_rate[52]=0.755 ;
women_existance_matching_rate[53]=0.755 ;
women_existance_matching_rate[54]=0.755 ;
women_existance_matching_rate[55]=0.789 ;
women_existance_matching_rate[56]=0.789 ;
women_existance_matching_rate[57]=0.789 ;
women_existance_matching_rate[58]=0.789 ;
women_existance_matching_rate[59]=0.789 ;
women_existance_matching_rate[60]=0.733 ;
women_existance_matching_rate[61]=0.733 ;
women_existance_matching_rate[62]=0.733 ;
women_existance_matching_rate[63]=0.733 ;
women_existance_matching_rate[64]=0.733 ;
women_existance_matching_rate[65]=0.663 ;
women_existance_matching_rate[66]=0.663 ;
women_existance_matching_rate[67]=0.663 ;
women_existance_matching_rate[68]=0.663 ;
women_existance_matching_rate[69]=0.663 ;
women_existance_matching_rate[70]=0.650 ;
women_existance_matching_rate[71]=0.650 ;
women_existance_matching_rate[72]=0.650 ;
women_existance_matching_rate[73]=0.650 ;
women_existance_matching_rate[74]=0.650 ;
women_existance_matching_rate[75]=0.462 ;
women_existance_matching_rate[76]=0.462 ;
women_existance_matching_rate[77]=0.462 ;
women_existance_matching_rate[78]=0.462 ;
women_existance_matching_rate[79]=0.462 ;
women_existance_matching_rate[80]=0.326 ;
women_existance_matching_rate[81]=0.326 ;
women_existance_matching_rate[82]=0.326 ;
women_existance_matching_rate[83]=0.326 ;
women_existance_matching_rate[84]=0.326 ;
women_existance_matching_rate[85]=0.093 ;
women_existance_matching_rate[86]=0.093 ;
women_existance_matching_rate[87]=0.093 ;
women_existance_matching_rate[88]=0.093 ;
women_existance_matching_rate[89]=0.093 ;
women_existance_matching_rate[90]=0.093 ;
women_existance_matching_rate[91]=0.093 ;
women_existance_matching_rate[92]=0.093 ;
women_existance_matching_rate[93]=0.093 ;
women_existance_matching_rate[94]=0.093 ;
women_existance_matching_rate[95]=0.093 ;
women_existance_matching_rate[96]=0.093 ;
women_existance_matching_rate[97]=0.093 ;
women_existance_matching_rate[98]=0.093 ;
women_existance_matching_rate[99]=0.093 ;
//有配偶離婚率 Divorce rates for married population
// 平成22年データ
// ○ 結婚している人に対する離婚率
// × 人口に対する離婚率
men_divorce_rate[0] = 0.0000 ;//結婚できないから、
men_divorce_rate[1] = 0.0000 ;//離婚もできない
men_divorce_rate[2] = 0.0000 ;
men_divorce_rate[3] = 0.0000 ;
men_divorce_rate[4] = 0.0000 ;
men_divorce_rate[5] = 0.0000 ;
men_divorce_rate[6] = 0.0000 ;
men_divorce_rate[7] = 0.0000 ;
men_divorce_rate[8] = 0.0000 ;
men_divorce_rate[9] = 0.0000 ;
men_divorce_rate[10] = 0.0000 ;
men_divorce_rate[11] = 0.0000 ;
men_divorce_rate[12] = 0.0000 ;
men_divorce_rate[13] = 0.0000 ;
men_divorce_rate[14] = 0.0000 ;
men_divorce_rate[15] = 0.0000 ;
men_divorce_rate[16] = 0.0000 ;
men_divorce_rate[17] = 0.0000 ;
men_divorce_rate[18] = 0.4809 ;
men_divorce_rate[19] = 0.4809 ;
men_divorce_rate[20] = 0.4705 ;
men_divorce_rate[21] = 0.4705 ;
men_divorce_rate[22] = 0.4705 ;
men_divorce_rate[23] = 0.4705 ;
men_divorce_rate[24] = 0.4705 ;
men_divorce_rate[25] = 0.2283 ;
men_divorce_rate[26] = 0.2283 ;
men_divorce_rate[27] = 0.2283 ;
men_divorce_rate[28] = 0.2283 ;
men_divorce_rate[29] = 0.2283 ;
men_divorce_rate[30] = 0.1521 ;
men_divorce_rate[31] = 0.1521 ;
men_divorce_rate[32] = 0.1521 ;
men_divorce_rate[33] = 0.1521 ;
men_divorce_rate[34] = 0.1521 ;
men_divorce_rate[35] = 0.1165 ;
men_divorce_rate[36] = 0.1165 ;
men_divorce_rate[37] = 0.1165 ;
men_divorce_rate[38] = 0.1165 ;
men_divorce_rate[39] = 0.1165 ;
men_divorce_rate[40] = 0.0939 ;
men_divorce_rate[41] = 0.0939 ;
men_divorce_rate[42] = 0.0939 ;
men_divorce_rate[43] = 0.0939 ;
men_divorce_rate[44] = 0.0939 ;
men_divorce_rate[45] = 0.0703 ;
men_divorce_rate[46] = 0.0703 ;
men_divorce_rate[47] = 0.0703 ;
men_divorce_rate[48] = 0.0703 ;
men_divorce_rate[49] = 0.0703 ;
men_divorce_rate[50] = 0.0495 ;
men_divorce_rate[51] = 0.0495 ;
men_divorce_rate[52] = 0.0495 ;
men_divorce_rate[53] = 0.0495 ;
men_divorce_rate[54] = 0.0495 ;
men_divorce_rate[55] = 0.0309 ;
men_divorce_rate[56] = 0.0309 ;
men_divorce_rate[57] = 0.0309 ;
men_divorce_rate[58] = 0.0309 ;
men_divorce_rate[59] = 0.0309 ;
men_divorce_rate[60] = 0.0194 ;
men_divorce_rate[61] = 0.0194 ;
men_divorce_rate[62] = 0.0194 ;
men_divorce_rate[63] = 0.0194 ;
men_divorce_rate[64] = 0.0194 ;
men_divorce_rate[65] = 0.0110 ;
men_divorce_rate[66] = 0.0110 ;
men_divorce_rate[67] = 0.0110 ;
men_divorce_rate[68] = 0.0110 ;
men_divorce_rate[69] = 0.0110 ;
men_divorce_rate[70] = 0.0040 ;
men_divorce_rate[71] = 0.0040 ;
men_divorce_rate[72] = 0.0040 ;
men_divorce_rate[73] = 0.0040 ;
men_divorce_rate[74] = 0.0040 ;
men_divorce_rate[75] = 0.0040 ;
men_divorce_rate[76] = 0.0040 ;
men_divorce_rate[77] = 0.0040 ;
men_divorce_rate[78] = 0.0040 ;
men_divorce_rate[79] = 0.0040 ;
men_divorce_rate[80] = 0.0040 ;
men_divorce_rate[81] = 0.0040 ;
men_divorce_rate[82] = 0.0040 ;
men_divorce_rate[83] = 0.0040 ;
men_divorce_rate[84] = 0.0040 ;
men_divorce_rate[85] = 0.0040 ;
men_divorce_rate[86] = 0.0040 ;
men_divorce_rate[87] = 0.0040 ;
men_divorce_rate[88] = 0.0040 ;
men_divorce_rate[89] = 0.0040 ;
men_divorce_rate[90] = 0.0040 ;
men_divorce_rate[91] = 0.0040 ;
men_divorce_rate[92] = 0.0040 ;
men_divorce_rate[93] = 0.0040 ;
men_divorce_rate[94] = 0.0040 ;
men_divorce_rate[95] = 0.0040 ;
men_divorce_rate[96] = 0.0040 ;
men_divorce_rate[97] = 0.0040 ;
men_divorce_rate[98] = 0.0040 ;
men_divorce_rate[99] = 0.0040 ;
women_divorce_rate[0] = 0.0000 ;//結婚できないから、
women_divorce_rate[1] = 0.0000 ;//離婚もできない
women_divorce_rate[2] = 0.0000 ;
women_divorce_rate[3] = 0.0000 ;
women_divorce_rate[4] = 0.0000 ;
women_divorce_rate[5] = 0.0000 ;
women_divorce_rate[6] = 0.0000 ;
women_divorce_rate[7] = 0.0000 ;
women_divorce_rate[8] = 0.0000 ;
women_divorce_rate[9] = 0.0000 ;
women_divorce_rate[10] = 0.0000 ;
women_divorce_rate[11] = 0.0000 ;
women_divorce_rate[12] = 0.0000 ;
women_divorce_rate[13] = 0.0000 ;
women_divorce_rate[14] = 0.0000 ;
women_divorce_rate[15] = 0.0000 ;
women_divorce_rate[16] = 0.8274 ;
women_divorce_rate[17] = 0.8274 ;
women_divorce_rate[18] = 0.8274 ;
women_divorce_rate[19] = 0.8274 ;
women_divorce_rate[20] = 0.4834 ;
women_divorce_rate[21] = 0.4834 ;
women_divorce_rate[22] = 0.4834 ;
women_divorce_rate[23] = 0.4834 ;
women_divorce_rate[24] = 0.4834 ;
women_divorce_rate[25] = 0.2288 ;
women_divorce_rate[26] = 0.2288 ;
women_divorce_rate[27] = 0.2288 ;
women_divorce_rate[28] = 0.2288 ;
women_divorce_rate[29] = 0.2288 ;
women_divorce_rate[30] = 0.1480 ;
women_divorce_rate[31] = 0.1480 ;
women_divorce_rate[32] = 0.1480 ;
women_divorce_rate[33] = 0.1480 ;
women_divorce_rate[34] = 0.1480 ;
women_divorce_rate[35] = 0.1090 ;
women_divorce_rate[36] = 0.1090 ;
women_divorce_rate[37] = 0.1090 ;
women_divorce_rate[38] = 0.1090 ;
women_divorce_rate[39] = 0.1090 ;
women_divorce_rate[40] = 0.0833 ;
women_divorce_rate[41] = 0.0833 ;
women_divorce_rate[42] = 0.0833 ;
women_divorce_rate[43] = 0.0833 ;
women_divorce_rate[44] = 0.0833 ;
women_divorce_rate[45] = 0.0560 ;
women_divorce_rate[46] = 0.0560 ;
women_divorce_rate[47] = 0.0560 ;
women_divorce_rate[48] = 0.0560 ;
women_divorce_rate[49] = 0.0560 ;
women_divorce_rate[50] = 0.0322 ;
women_divorce_rate[51] = 0.0322 ;
women_divorce_rate[52] = 0.0322 ;
women_divorce_rate[53] = 0.0322 ;
women_divorce_rate[54] = 0.0322 ;
women_divorce_rate[55] = 0.0172 ;
women_divorce_rate[56] = 0.0172 ;
women_divorce_rate[57] = 0.0172 ;
women_divorce_rate[58] = 0.0172 ;
women_divorce_rate[59] = 0.0172 ;
women_divorce_rate[60] = 0.0113 ;
women_divorce_rate[61] = 0.0113 ;
women_divorce_rate[62] = 0.0113 ;
women_divorce_rate[63] = 0.0113 ;
women_divorce_rate[64] = 0.0113 ;
women_divorce_rate[65] = 0.0073 ;
women_divorce_rate[66] = 0.0073 ;
women_divorce_rate[67] = 0.0073 ;
women_divorce_rate[68] = 0.0073 ;
women_divorce_rate[69] = 0.0073 ;
women_divorce_rate[70] = 0.0028 ;
women_divorce_rate[71] = 0.0028 ;
women_divorce_rate[72] = 0.0028 ;
women_divorce_rate[73] = 0.0028 ;
women_divorce_rate[74] = 0.0028 ;
women_divorce_rate[75] = 0.0028 ;
women_divorce_rate[76] = 0.0028 ;
women_divorce_rate[77] = 0.0028 ;
women_divorce_rate[78] = 0.0028 ;
women_divorce_rate[79] = 0.0028 ;
women_divorce_rate[80] = 0.0028 ;
women_divorce_rate[81] = 0.0028 ;
women_divorce_rate[82] = 0.0028 ;
women_divorce_rate[83] = 0.0028 ;
women_divorce_rate[84] = 0.0028 ;
women_divorce_rate[85] = 0.0028 ;
women_divorce_rate[86] = 0.0028 ;
women_divorce_rate[87] = 0.0028 ;
women_divorce_rate[88] = 0.0028 ;
women_divorce_rate[89] = 0.0028 ;
women_divorce_rate[90] = 0.0028 ;
women_divorce_rate[91] = 0.0028 ;
women_divorce_rate[92] = 0.0028 ;
women_divorce_rate[93] = 0.0028 ;
women_divorce_rate[94] = 0.0028 ;
women_divorce_rate[95] = 0.0028 ;
women_divorce_rate[96] = 0.0028 ;
women_divorce_rate[97] = 0.0028 ;
women_divorce_rate[98] = 0.0028 ;
women_divorce_rate[99] = 0.0028 ;
// 再婚率 (2010年)
// × 離婚している人に対する再婚率
// ○ 世代人口(未婚、結婚、離婚関係なし)に対する再婚率
// ということで、計算式に注意しなければならない。
// (というか、なんで、最初からそういう数値にしないんだ!)
// 離婚している人に対する再婚率 (に変換する式)
// =
// 再婚率 x その世代の人口数 / 離婚(×未婚、結婚)人口
// としなければなない
//表６－６ 性，年齢（５歳階級）別再婚率：1930～2010年
//（‰）
//年 齢 2010年
men_remarrige_ratio[0] = 0.0000;
men_remarrige_ratio[1] = 0.0000;
men_remarrige_ratio[2] = 0.0000;
men_remarrige_ratio[3] = 0.0000;
men_remarrige_ratio[4] = 0.0000;
men_remarrige_ratio[5] = 0.0000;
men_remarrige_ratio[6] = 0.0000;
men_remarrige_ratio[7] = 0.0000;
men_remarrige_ratio[8] = 0.0000;
men_remarrige_ratio[9] = 0.0000;
men_remarrige_ratio[10] = 0.0000;
men_remarrige_ratio[11] = 0.0000;
men_remarrige_ratio[12] = 0.0000;
men_remarrige_ratio[13] = 0.0000;
men_remarrige_ratio[14] = 0.0000;
men_remarrige_ratio[15] = 0.0000;
men_remarrige_ratio[16] = 0.0000;
men_remarrige_ratio[17] = 0.0000;
men_remarrige_ratio[18] = 0.0001;
men_remarrige_ratio[19] = 0.0001;
men_remarrige_ratio[20] = 0.0048;
men_remarrige_ratio[21] = 0.0048;
men_remarrige_ratio[22] = 0.0048;
men_remarrige_ratio[23] = 0.0048;
men_remarrige_ratio[24] = 0.0048;
men_remarrige_ratio[25] = 0.0226;
men_remarrige_ratio[26] = 0.0226;
men_remarrige_ratio[27] = 0.0226;
men_remarrige_ratio[28] = 0.0226;
men_remarrige_ratio[29] = 0.0226;
men_remarrige_ratio[30] = 0.0448;
men_remarrige_ratio[31] = 0.0448;
men_remarrige_ratio[32] = 0.0448;
men_remarrige_ratio[33] = 0.0448;
men_remarrige_ratio[34] = 0.0448;
men_remarrige_ratio[35] = 0.0476;
men_remarrige_ratio[36] = 0.0476;
men_remarrige_ratio[37] = 0.0476;
men_remarrige_ratio[38] = 0.0476;
men_remarrige_ratio[39] = 0.0476;
men_remarrige_ratio[40] = 0.0371;
men_remarrige_ratio[41] = 0.0371;
men_remarrige_ratio[42] = 0.0371;
men_remarrige_ratio[43] = 0.0371;
men_remarrige_ratio[44] = 0.0371;
men_remarrige_ratio[45] = 0.0261;
men_remarrige_ratio[46] = 0.0261;
men_remarrige_ratio[47] = 0.0261;
men_remarrige_ratio[48] = 0.0261;
men_remarrige_ratio[49] = 0.0261;
men_remarrige_ratio[50] = 0.0180;
men_remarrige_ratio[51] = 0.0180;
men_remarrige_ratio[52] = 0.0180;
men_remarrige_ratio[53] = 0.0180;
men_remarrige_ratio[54] = 0.0180;
men_remarrige_ratio[55] = 0.0123;
men_remarrige_ratio[56] = 0.0123;
men_remarrige_ratio[57] = 0.0123;
men_remarrige_ratio[58] = 0.0123;
men_remarrige_ratio[59] = 0.0123;
men_remarrige_ratio[60] = 0.0091;
men_remarrige_ratio[61] = 0.0091;
men_remarrige_ratio[62] = 0.0091;
men_remarrige_ratio[63] = 0.0091;
men_remarrige_ratio[64] = 0.0091;
men_remarrige_ratio[65] = 0.0056;
men_remarrige_ratio[66] = 0.0056;
men_remarrige_ratio[67] = 0.0056;
men_remarrige_ratio[68] = 0.0056;
men_remarrige_ratio[69] = 0.0056;
men_remarrige_ratio[70] = 0.0025;
men_remarrige_ratio[71] = 0.0025;
men_remarrige_ratio[72] = 0.0025;
men_remarrige_ratio[73] = 0.0025;
men_remarrige_ratio[74] = 0.0025;
men_remarrige_ratio[75] = 0.0025;
men_remarrige_ratio[76] = 0.0025;
men_remarrige_ratio[77] = 0.0025;
men_remarrige_ratio[78] = 0.0025;
men_remarrige_ratio[79] = 0.0025;
men_remarrige_ratio[80] = 0.0025;
men_remarrige_ratio[81] = 0.0025;
men_remarrige_ratio[82] = 0.0025;
men_remarrige_ratio[83] = 0.0025;
men_remarrige_ratio[84] = 0.0025;
men_remarrige_ratio[85] = 0.0025;
men_remarrige_ratio[86] = 0.0025;
men_remarrige_ratio[87] = 0.0025;
men_remarrige_ratio[88] = 0.0025;
men_remarrige_ratio[89] = 0.0025;
men_remarrige_ratio[90] = 0.0025;
men_remarrige_ratio[91] = 0.0025;
men_remarrige_ratio[92] = 0.0025;
men_remarrige_ratio[93] = 0.0025;
men_remarrige_ratio[94] = 0.0025;
men_remarrige_ratio[95] = 0.0025;
men_remarrige_ratio[96] = 0.0025;
men_remarrige_ratio[97] = 0.0025;
men_remarrige_ratio[98] = 0.0025;
men_remarrige_ratio[99] = 0.0025;
women_remarrige_ratio[0] = 0.0000;
women_remarrige_ratio[1] = 0.0000;
women_remarrige_ratio[2] = 0.0000;
women_remarrige_ratio[3] = 0.0000;
women_remarrige_ratio[4] = 0.0000;
women_remarrige_ratio[5] = 0.0000;
women_remarrige_ratio[6] = 0.0000;
women_remarrige_ratio[7] = 0.0000;
women_remarrige_ratio[8] = 0.0000;
women_remarrige_ratio[9] = 0.0000;
women_remarrige_ratio[10] = 0.0000;
women_remarrige_ratio[11] = 0.0000;
women_remarrige_ratio[12] = 0.0000;
women_remarrige_ratio[13] = 0.0000;
women_remarrige_ratio[14] = 0.0000;
women_remarrige_ratio[15] = 0.0000;
women_remarrige_ratio[16] = 0.0004;
women_remarrige_ratio[17] = 0.0004;
women_remarrige_ratio[18] = 0.0004;
women_remarrige_ratio[19] = 0.0004;
women_remarrige_ratio[20] = 0.0103;
women_remarrige_ratio[21] = 0.0103;
women_remarrige_ratio[22] = 0.0103;
women_remarrige_ratio[23] = 0.0103;
women_remarrige_ratio[24] = 0.0103;
women_remarrige_ratio[25] = 0.0345;
women_remarrige_ratio[26] = 0.0345;
women_remarrige_ratio[27] = 0.0345;
women_remarrige_ratio[28] = 0.0345;
women_remarrige_ratio[29] = 0.0345;
women_remarrige_ratio[30] = 0.0501;
women_remarrige_ratio[31] = 0.0501;
women_remarrige_ratio[32] = 0.0501;
women_remarrige_ratio[33] = 0.0501;
women_remarrige_ratio[34] = 0.0501;
women_remarrige_ratio[35] = 0.0438;
women_remarrige_ratio[36] = 0.0438;
women_remarrige_ratio[37] = 0.0438;
women_remarrige_ratio[38] = 0.0438;
women_remarrige_ratio[39] = 0.0438;
women_remarrige_ratio[40] = 0.0269;
women_remarrige_ratio[41] = 0.0269;
women_remarrige_ratio[42] = 0.0269;
women_remarrige_ratio[43] = 0.0269;
women_remarrige_ratio[44] = 0.0269;
women_remarrige_ratio[45] = 0.0176;
women_remarrige_ratio[46] = 0.0176;
women_remarrige_ratio[47] = 0.0176;
women_remarrige_ratio[48] = 0.0176;
women_remarrige_ratio[49] = 0.0176;
women_remarrige_ratio[50] = 0.0115;
women_remarrige_ratio[51] = 0.0115;
women_remarrige_ratio[52] = 0.0115;
women_remarrige_ratio[53] = 0.0115;
women_remarrige_ratio[54] = 0.0115;
women_remarrige_ratio[55] = 0.0069;
women_remarrige_ratio[56] = 0.0069;
women_remarrige_ratio[57] = 0.0069;
women_remarrige_ratio[58] = 0.0069;
women_remarrige_ratio[59] = 0.0069;
women_remarrige_ratio[60] = 0.0043;
women_remarrige_ratio[61] = 0.0043;
women_remarrige_ratio[62] = 0.0043;
women_remarrige_ratio[63] = 0.0043;
women_remarrige_ratio[64] = 0.0043;
women_remarrige_ratio[65] = 0.0025;
women_remarrige_ratio[66] = 0.0025;
women_remarrige_ratio[67] = 0.0025;
women_remarrige_ratio[68] = 0.0025;
women_remarrige_ratio[69] = 0.0025;
women_remarrige_ratio[70] = 0.0007;
women_remarrige_ratio[71] = 0.0007;
women_remarrige_ratio[72] = 0.0007;
women_remarrige_ratio[73] = 0.0007;
women_remarrige_ratio[74] = 0.0007;
women_remarrige_ratio[75] = 0.0007;
women_remarrige_ratio[76] = 0.0007;
women_remarrige_ratio[77] = 0.0007;
women_remarrige_ratio[78] = 0.0007;
women_remarrige_ratio[79] = 0.0007;
women_remarrige_ratio[80] = 0.0007;
women_remarrige_ratio[81] = 0.0007;
women_remarrige_ratio[82] = 0.0007;
women_remarrige_ratio[83] = 0.0007;
women_remarrige_ratio[84] = 0.0007;
women_remarrige_ratio[85] = 0.0007;
women_remarrige_ratio[86] = 0.0007;
women_remarrige_ratio[87] = 0.0007;
women_remarrige_ratio[88] = 0.0007;
women_remarrige_ratio[89] = 0.0007;
women_remarrige_ratio[90] = 0.0007;
women_remarrige_ratio[91] = 0.0007;
women_remarrige_ratio[92] = 0.0007;
women_remarrige_ratio[93] = 0.0007;
women_remarrige_ratio[94] = 0.0007;
women_remarrige_ratio[95] = 0.0007;
women_remarrige_ratio[96] = 0.0007;
women_remarrige_ratio[97] = 0.0007;
women_remarrige_ratio[98] = 0.0007;
women_remarrige_ratio[99] = 0.0007;
}