CF285E DP+组合数学

本文详细介绍了使用递推算法解决排列组合问题的过程,包括关键步骤和代码实现。通过实例分析,阐述了如何利用递推关系式进行计算,并讨论了优化细节,如模运算的应用。文章还分享了一个具体的ACcode实例,展示了完整的代码实现和优化技巧。

很经典的递推,将当前第i+1个位置是否放了i+2这个数记录下来,t=f[i][j][x][y];

1、如果i+1上为i+2则:if (i+2<=n) add(f[i+1][j+1][y][1],t);

2、如果i+1上为i则:if (x==0) add(f[i+1][j+1][y][0],t);

3、如果当前位为其他数:add(f[i+1][j][y][0],t);


开始用减去乘积的时候没用模运算,用循环加上来果断超时了,细节仍然是很大的一个问题,又错了一次,记录下来敲打


ACcode:

#include<cstdio>
#include<cstring>
#include<cmath>

typedef long long LL;
const int ns=1010;
const int MOD=1000000007;

int n,k,t;
int fac[ns],C[ns][ns],ans[ns];
int f[ns][ns][2][2];
LL dp[ns];

void prepare()
{
    fac[0]=C[0][0]=1;
    for (int i=1; i<ns; i++)
        C[i][0]=1,fac[i]=((LL)fac[i-1]*i)%MOD;
    for (int i=1; i<ns; i++)
    for (int j=1; j<=i; j++)
    {
        C[i][j]=C[i-1][j-1]+C[i-1][j];
        if (C[i][j]>=MOD) C[i][j]-=MOD;
    }
}

void add(int &a,int b)
{
    a+=b;
    if (a>=MOD) a-=MOD;
}

int main()
{
    prepare();
    while (~scanf("%d%d",&n,&k))
    {
        memset(f,0,sizeof(f));
        memset(dp,0,sizeof(dp));
        f[1][0][0][0]=1;
        if (n>1) f[1][1][0][1]=1;
        for (int i=1; i<n; i++)
        for (int j=0; j<=i; j++)
        for (int x=0; x<2; x++)
        for (int y=0; y<2; y++)
        {
            t=f[i][j][x][y];
            add(f[i+1][j][y][0],t);
            if (x==0) add(f[i+1][j+1][y][0],t);
            if (i+2<=n) add(f[i+1][j+1][y][1],t);
        }
        for (int i=0; i<=n; i++)
        {
            dp[i]=(LL)(f[n][i][0][0]+f[n][i][0][1]+
                       f[n][i][1][0]+f[n][i][1][1])*(LL)fac[n-i]%MOD;
        }
        for (int i=n-1; i>=k; i--)
        for (int j=n; j>i; j--)
        {
            dp[i]-=((LL)C[j][i]*dp[j])%MOD;
            if (dp[i]<0) dp[i]+=MOD;
        }
        printf("%I64d\n",dp[k]);
    }
    return 0;
}


import copy import pandas as pd import numpy as np from scipy.optimize import curve_fit as cf from matplotlib import pyplot as plt ## 将不同转速下的流量-功率曲线和流量-静压曲线拟合成三次型函数 ## 系统的静压曲线使用指数型关系式代替 ALPHA = 0.2 BETA = 0.5 class FanCurve: def __init__(self): self.curve_power = { 1280: [np.float64(-1.0939824906995037e-09), np.float64(2.333022185311005e-05), np.float64(-0.06796629659302983), np.float64(2055.3002100486133)], 1440: [np.float64(-1.136696332147217e-09), np.float64(2.7660598448342913e-05), np.float64(-0.09613228325870021), np.float64(2922.59538328202)], 1500: [np.float64(-1.7603856529124879e-09), np.float64(4.4241229797522305e-05), np.float64(-0.14983705834350444), np.float64(5160.7745241685625)], 1600: [np.float64(-1.1701720186561717e-09), np.float64(3.0605089172086764e-05), np.float64(-0.0989935007927685), np.float64(3869.5082357198503)] } self.curve_dp = { 1280: [np.float64(-3.267975749307142e-10), np.float64(5.2912277771354885e-06), np.float64(-0.03403187464621037), np.float64(741.9041721827268)], 1440: [np.float64(-2.422194407553435e-10), np.float64(3.7698924924279502e-06), np.float64(-0.02559742162953265), np.float64(931.5430549263344)], 1500: [np.float64(-2.3878745827961e-10), np.float64(3.921270379695461e-06), np.float64(-0.02640953385995719), np.float64(1002.1261618905066)], 1600: [np.float64(-2.2348965671136672e-10), np.float64(3.886724444389961e-06), np.float64(-0.027754898177200903), np.float64(1147.392301537067)] } self.speed = [1280, 1440, 1500, 1600] # 有顺序的 不要改 self.alpha = ALPHA self.beta = BETA self.q_min = 0 self.q_max = 24000 def pressure_q(self, q): return self.alpha * q ** self.beta @staticmethod def curve_function(q, a, b, c, d): return a * q **3 + b * q **2 + c * q + d @staticmethod def mk_function(a, b, c, d): # 此函数适用于不插值的计算 def curve_function(q): return a * q ** 3 + b * q ** 2 + c * q + d return curve_function @staticmethod def mk_inter_function(percent, a, b, c, d, e, f, g, h): ## 如果使用插值,就用这个方法构造函数 ## abcd是左侧值的参数,efgh是右侧的 def inter_curve_function(q): l_value = a * q ** 3 + b * q ** 2 + c * q + d r_value = e * q ** 3 + f * q ** 2 + g * q + h return (1 - percent) * l_value + percent * r_value return inter_curve_function @staticmethod def mk_outside_function(coefficient, a, b, c, d): def curve_function(q): return (a * q ** 3 + b * q ** 2 + c * q + d) * coefficient return curve_function def get_functions(self, n): """ 使用转速和系统的阻力特性计算体积流量 转速在性能曲线范围内的采用插值处理 转速在性能曲线外的采用风机相似定律 :param n: :return: """ if 1280 <= n <= 1600: ## 插值得出风机的静压,电功率 if n in self.speed: ## 给定的转速在风机性能曲线内,不需要插值 power_params = self.curve_power[n] power_function = self.mk_function(*power_params) dp_params = self.curve_dp[n] dp_function = self.mk_function(*dp_params) else: ## 给定的转速不在风机性能曲线上,则需要进行插值 new_speed = copy.deepcopy(self.speed) new_speed.append(n) new_speed.sort() idx = 0 for i in range(len(new_speed)): if n == new_speed[i]: idx = i break l_speed = new_speed[idx - 1] r_speed = new_speed[idx + 1] l_power_params = self.curve_power[l_speed] r_power_params = self.curve_power[r_speed] l_dp_params = self.curve_dp[l_speed] r_dp_params = self.curve_dp[r_speed] percent = (n - l_speed) / (r_speed - l_speed) power_function = self.mk_inter_function(percent, *l_power_params, *r_power_params) dp_function = self.mk_inter_function(percent, *l_dp_params, *r_dp_params) else: ## 使用风机相似定律求出风机电功率、静压 if n < 1280: volume_coefficient = n / 1280 else: volume_coefficient = n / 1600 pressure_coefficient = volume_coefficient ** 2 power_coefficient = volume_coefficient ** 3 dp_params = self.curve_dp[1280] power_params = self.curve_power[1280] dp_function = self.mk_outside_function(pressure_coefficient, *dp_params) power_function = self.mk_outside_function(power_coefficient, *power_params) return {"dp_function": dp_function, "power_function": power_function} def solve(self, f, g, q_init=10000, convergence = 1e-06, max_iter = 10000): ## 暂时不对无解情况进行规避 count = 0 def difference(q): return f(q) - g(q) l, r = self.q_min, self.q_max if difference(self.q_min) == 0: return self.q_min if difference(self.q_max) == 0: return self.q_max while True: if difference(l) * difference(r) > 0: raise ValueError("使用二分法时,似乎存在多解,无法求得确切值") if 0 < difference(q_init) <= convergence: break if difference(q_init) * difference(l) > 0: ## 跟左边的值同号 l = q_init q_init = (l + r) / 2 else: r = q_init q_init = (l + r) / 2 count += 1 if count > max_iter: raise TimeoutError("迭代次数已经超过了最大迭代次数") return q_init def run(self, n): functions = self.get_functions(n) q = self.solve(functions["dp_function"], self.pressure_q) return q if __name__ == '__main__': # ## 读取数据 # dataframe_dp = pd.read_excel(r"E:\projects\air_conditioner\fan\v_dp_1600.xlsx") # # # dataframe_power = pd.read_excel(r"E:\projects\air_conditioner\fan\power_1600.xlsx") # # ## 拟合 # # def f_power(x, a, b, c, d): # return a * x **3 + b * x **2 + c * x + d # # # for file_name in ["power_1280", "power_1440", "power_1500", "power_1600"]: # file_path = fr"E:\projects\air_conditioner\fan\{file_name}.xlsx" # dataframe_power = pd.read_excel(file_path) # # x_data = np.array(dataframe_power['x']) # y_data = np.array(dataframe_power['y']) # # a, b, c, d = cf(f_power, x_data, y_data)[0] # xx = [a, b, c, d] # print(file_name, f"x={xx}") n_set = 1400 solution = FanCurve() q = solution.run(n_set) p = solution.pressure_q(q) functions = solution.get_functions(n_set) print("性能曲线压力:", functions["dp_function"](q)) print(q) print("阻力压力:", p) 帮我看看这段代码如何对流量q进行求解,输入量是什么,求解方法是什么
最新发布
07-16
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