equation.cs

运用ASP.net C#语言对SQL数据库表格中的数据进行运算处理。

using System;
using System.Data;
using System.Configuration;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Web.UI.HtmlControls;

/// <summary>
/// equation 的摘要说明
/// </summary>
public class equation
{
	public equation()
	{
		//
		// TODO: 在此处添加构造函数逻辑
		//
	}
    public void get_data(DataSet ds, DataSet dt, string blm, float start_rate, float over_rate)
    {
        switch (blm)
        {
            case "C_GDP":
                get_GDITOGDP(ds, dt, blm, start_rate, over_rate); break;
            case "C_CONS":
                get_GDITOCONS(ds, dt, blm, start_rate,over_rate); break;
            case "C_GFCF":
                get_GDITOGFCF(ds, dt, blm, start_rate,over_rate); break;
            case "C_GDP2I":
                get_GDITOGDP2I(ds, dt, blm, start_rate,over_rate); break;
            case "C_GDP2C":
                get_GDITOGDP2C(ds, dt, blm, start_rate,over_rate); break;
            case "C_GDP3":
                get_GDITOGDP3(ds, dt, blm, start_rate,over_rate); break;
            case "C_PCDI":
                get_GDITOPCDI(ds, dt, blm, start_rate,over_rate); break;
            case "C_TLM":
                get_GDITOTLM(ds, dt, blm, start_rate,over_rate); break;
            default:
                get_GDITOGDP(ds, dt, blm, start_rate,over_rate); break;
        }
    }//固定资产的选择函数
    public void get_GDITOGDP(DataSet ds, DataSet dt, string blm, float start_rate, float over_rate)
    {
        int Count_R = ds.Tables[0].Rows.Count;//ds的行数
        int Count_C = ds.Tables[0].Columns.Count;//ds的列数
        int dt_Count_C = dt.Tables[0].Columns.Count;//读取dt的列数

        //定义变量
        int i, j;
        int Rate_num = 1;
        float rate_data = start_rate;

        //定义数组
        float[,] values = new float[Count_R + 3, Count_C];//这里加3是因为，需要预测未来三年的数据
        float[] rate = new float[11];

        //计算变化率的个数
        rate[0] = 0;
        for (i = 1; i < Convert.ToInt16(2 * (over_rate * 100 - start_rate * 100 + 1) + 1); i++)
        {
            Rate_num = Rate_num + 1;
            if (i % 2 == 1)
                rate[i] = -1 * rate_data;
            if (i % 2 == 0)
            {
                rate[i] = rate_data;
                rate_data = Convert.ToSingle(rate_data + 0.01);
            }
        }

        //读取ds数据集里的数据到values数组中
        for (i = 0; i < Count_R; i++)
            for (j = 0; j < Count_C; j++)
                values[i, j] = Convert.ToSingle(ds.Tables[0].Rows[i][j].ToString());

        //用移动平均法计算未来三年的数据
        for (i = Count_R; i < (Count_R + 3); i++)
        {
            values[i, 0] = values[i - 1, 0] + 1;
            for (j = 1; j < Count_C; j++)
            {
                values[i, j] = (values[i - 3, j] + values[i - 2, j] + values[i - 1, j]) / 3;
            }
        }

        //开始计算数据
        float[,] data = new float[Rate_num, dt_Count_C];
        for (i = 0; i < Rate_num; i++)
        {
            data[i, 0] = rate[i];
            for (j = 1; j < dt_Count_C - 2; j++)
            {
                values[j - 1, 1] = Convert.ToSingle(values[j - 1, 1] * (1 - rate[i]));
                data[i, j] = Convert.ToSingle(0.04623 * values[j - 1, Count_C - 12] + 0.118098 * values[j - 1, Count_C - 11] + 0.095575 * values[j - 1, Count_C - 10] + 0.068656 * values[j - 1, Count_C - 9] + 0.143922 * values[j - 1, Count_C - 8] + 0.108141 * values[j - 1, Count_C - 7] + 0.138517 * values[j - 1, Count_C - 6] + 0.108204 * values[j - 1, Count_C - 5] + 0.270564 * values[j - 1, Count_C - 4] + 0.063256 * values[j - 1, Count_C - 3] + 0.04791 * values[j - 1, Count_C - 2] + 0.084313 * values[j - 1, Count_C - 1] - 29.3978);
            }
        }
        //把数据存储到dt数据集里面
        for (i = 0; i < Rate_num; i++)
        {
            DataRow newRow = dt.Tables[dt.Tables[0].ToString()].NewRow();
            newRow["rate"] = Math.Round(rate[i], 2);
            for (j = 1; j < dt_Count_C - 2; j++)
                newRow["year" + j] = data[i, j];
            newRow["blm"] = blm;
            newRow["code"] = 1;
            dt.Tables[dt.Tables[0].ToString()].Rows.Add(newRow);
        }
    }//固定资产对全市生产总值的影响
    public void get_GDITOCONS(DataSet ds, DataSet dt, string blm, float start_rate, float over_rate)
    {
        int Count_R = ds.Tables[0].Rows.Count;//ds的行数
        int Count_C = ds.Tables[0].Columns.Count;//ds的列数
        int dt_Count_C = dt.Tables[0].Columns.Count;//读取dt的列数

        //定义变量
        int i, j;
        int Rate_num = 1;
        float rate_data = start_rate;

        //定义数组
        float[,] values = new float[Count_R + 3, Count_C];//这里加3是因为，需要预测未来三年的数据
        float[] rate = new float[11];

        //计算变化率的个数
        rate[0] = 0;
        for (i = 1; i < Convert.ToInt16(2 * (over_rate * 100 - start_rate * 100 + 1) + 1); i++)
        {
            Rate_num = Rate_num + 1;
            if (i % 2 == 1)
                rate[i] = -1 * rate_data;
            if (i % 2 == 0)
            {
                rate[i] = rate_data;
                rate_data = Convert.ToSingle(rate_data + 0.01);
            }
        }

        //读取ds数据集里的数据到values数组中
        for (i = 0; i < Count_R; i++)
            for (j = 0; j < Count_C; j++)
                values[i, j] = Convert.ToSingle(ds.Tables[0].Rows[i][j].ToString());

        //用移动平均法计算未来三年的数据
        for (i = Count_R; i < (Count_R + 3); i++)
        {
            values[i, 0] = values[i - 1, 0] + 1;
            for (j = 1; j < Count_C; j++)
            {
                values[i, j] = (values[i - 3, j] + values[i - 2, j] + values[i - 1, j]) / 3;
            }
        }

        //开始计算数据
        float[,] data = new float[Rate_num, dt_Count_C];
        for (i = 0; i < Rate_num; i++)
        {
            data[i, 0] = rate[i];
            for (j = 1; j < dt_Count_C - 2; j++)
            {
                values[j - 1, 1] = Convert.ToSingle(values[j - 1, 1] * (1 - rate[i]));
                data[i, j] = Convert.ToSingle(-0.02802 * values[j - 1, Count_C - 10] + 0.408724 * values[j - 1, Count_C - 9] - 0.10582 * values[j - 1, Count_C - 8] + 0.164543 * values[j - 1, Count_C - 7] + 0.250406 * values[j - 1, Count_C - 6] + 0.164543 * values[j - 1, Count_C - 5] - 0.13524 * values[j - 1, Count_C - 4] + 0.206824 * values[j - 1, Count_C - 3] + 0.076581 * values[j - 1, Count_C - 2] + 0.036754 * values[j - 1, Count_C - 1] - 4.44492);
            }
        }
        //把数据存储到dt数据集里面
        for (i = 0; i < Rate_num; i++)
        {
            DataRow newRow = dt.Tables[dt.Tables[0].ToString()].NewRow();
            newRow["rate"] = Math.Round(rate[i], 2);
            for (j = 1; j < dt_Count_C - 2; j++)
                newRow["year" + j] = data[i, j];
            newRow["blm"] = blm;
            newRow["code"] = 1;
            dt.Tables[dt.Tables[0].ToString()].Rows.Add(newRow);
        }
    }
    public void get_GDITOGFCF(DataSet ds, DataSet dt, string blm, float start_rate, float over_rate)
    {
        int Count_R = ds.Tables[0].Rows.Count;//ds的行数
        int Count_C = ds.Tables[0].Columns.Count;//ds的列数
        int dt_Count_C = dt.Tables[0].Columns.Count;//读取dt的列数

        //定义变量
        int i, j;
        int Rate_num = 1;
        float rate_data = start_rate;

        //定义数组
        float[,] values = new float[Count_R + 3, Count_C];//这里加3是因为，需要预测未来三年的数据
        float[] rate = new float[11];

        //计算变化率的个数
        rate[0] = 0;
        for (i = 1; i < Convert.ToInt16(2 * (over_rate * 100 - start_rate * 100 + 1) + 1); i++)
        {
            Rate_num = Rate_num + 1;
            if (i % 2 == 1)
                rate[i] = -1 * rate_data;
            if (i % 2 == 0)
            {
                rate[i] = rate_data;
                rate_data = Convert.ToSingle(rate_data + 0.01);
            }
        }

        //读取ds数据集里的数据到values数组中
        for (i = 0; i < Count_R; i++)
            for (j = 0; j < Count_C; j++)
                values[i, j] = Convert.ToSingle(ds.Tables[0].Rows[i][j].ToString());

        //用移动平均法计算未来三年的数据
        for (i = Count_R; i < (Count_R + 3); i++)
        {
            values[i, 0] = values[i - 1, 0] + 1;
            for (j = 1; j < Count_C; j++)
            {
                values[i, j] = (values[i - 3, j] + values[i - 2, j] + values[i - 1, j]) / 3;
            }
        }

        //开始计算数据
        float[,] data = new float[Rate_num, dt_Count_C];
        for (i = 0; i < Rate_num; i++)
        {
            data[i, 0] = rate[i];
            for (j = 1; j < dt_Count_C - 2; j++)
            {
                values[j - 1, 1] = Convert.ToSingle(values[j - 1, 1] * (1 - rate[i]));
                data[i, j] = Convert.ToSingle(0.162577 * values[j - 1, Count_C - 6] + 0.003684 * values[j - 1, Count_C - 5] + 0.14316 * values[j - 1, Count_C - 4] + 0.313774 * values[j - 1, Count_C - 3] + 0.226303 * values[j - 1, Count_C - 2] + 0.235526 * values[j - 1, Count_C - 1] - 7.95947);
            }
        }
        //把数据存储到dt数据集里面
        for (i = 0; i < Rate_num; i++)
        {
            DataRow newRow = dt.Tables[dt.Tables[0].ToString()].NewRow();
            newRow["rate"] = Math.Round(rate[i], 2);
            for (j = 1; j < dt_Count_C - 2; j++)
                newRow["year" + j] = data[i, j];
            newRow["blm"] = blm;
            newRow["code"] = 1;
            dt.Tables[dt.Tables[0].ToString()].Rows.Add(newRow);
        }
    }
    public void get_GDITOGDP2I(DataSet ds, DataSet dt, string blm, float start_rate, float over_rate)
    {
        int Count_R = ds.Tables[0].Rows.Count;//ds的行数
        int Count_C = ds.Tables[0].Columns.Count;//ds的列数
        int dt_Count_C = dt.Tables[0].Columns.Count;//读取dt的列数

        //定义变量
        int i, j;
        int Rate_num = 1;
        float rate_data = start_rate;

        //定义数组
        float[,] values = new float[Count_R + 3, Count_C];//这里加3是因为，需要预测未来三年的数据
        float[] rate = new float[11];

        //计算变化率的个数
        rate[0] = 0;
        for (i = 1; i < Convert.ToInt16(2 * (over_rate * 100 - start_rate * 100 + 1) + 1); i++)
        {
            Rate_num = Rate_num + 1;
            if (i % 2 == 1)
                rate[i] = -1 * rate_data;
            if (i % 2 == 0)
            {
                rate[i] = rate_data;
                rate_data = Convert.ToSingle(rate_data + 0.01);
            }
        }

        //读取ds数据集里的数据到values数组中
        for (i = 0; i < Count_R; i++)
            for (j = 0; j < Count_C; j++)
                values[i, j] = Convert.ToSingle(ds.Tables[0].Rows[i][j].ToString());

        //用移动平均法计算未来三年的数据
        for (i = Count_R; i < (Count_R + 3); i++)
        {
            values[i, 0] = values[i - 1, 0] + 1;
            for (j = 1; j < Count_C; j++)
            {
                values[i, j] = (values[i - 3, j] + values[i - 2, j] + values[i - 1, j]) / 3;
            }
        }

        //开始计算数据
        float[,] data = new float[Rate_num, dt_Count_C];
        for (i = 0; i < Rate_num; i++)
        {
            data[i, 0] = rate[i];
            for (j = 1; j < dt_Count_C - 2; j++)
            {
                values[j - 1, 1] = Convert.ToSingle(values[j - 1, 1] * (1 - rate[i]));
                data[i, j] = Convert.ToSingle(0.367295 * values[j - 1, Count_C - 4] + 0.941441 * values[j - 1, Count_C - 3] - 0.15105 * values[j - 1, Count_C - 2] - 0.29734 * values[j - 1, Count_C - 1] + 14.27036);
            }
        }
        //把数据存储到dt数据集里面
        for (i = 0; i < Rate_num; i++)
        {
            DataRow newRow = dt.Tables[dt.Tables[0].ToString()].NewRow();
            newRow["rate"] = Math.Round(rate[i], 2);
            for (j = 1; j < dt_Count_C - 2; j++)
                newRow["year" + j] = data[i, j];