### 计蒜客 17308.Weather Patterns

582

# 题目

Consider a system which is described at any time as being in one of a set of $N$ distinct states, $1,2,3,...,N$. We denote the time instants associated with state changes as $t = 1,2,...$, and the actual state at time $t$ as $a_{ij} = p = [s_{i}=j | s_{i-1}=i], 1le i,j le N$.

For the special case of a discrete, first order, Markovchain, the probabilistic description for the current state (at time $t$) and the predecessor state is $s_{t}$. Furthermore we only consider those processes being independent of time, thereby leading to the set of state transition probability $a_{ij}$ of the form: with the properties $a_{ij} geq 0$ and $sum_{i=1}^{N} A_{ij} = 1 $.

The stochastic process can be called an observable Markovmodel. Now, let us consider the problem of a simple 4-state Markov model of weather. We assume that once a day (e.g., at noon), the weather is observed as being one of the following:

- State $1$: snow
- State $2$: rain
- State $3$: cloudy
- State $4$: sunny

The matrix $A$ of state transition probabilities is:

$A = {a_{ij}}= begin{Bmatrix} a_{11}& a_{12}& a_{13}&a_{14} \\

a_{21}&a_{22}&a_{23}&a_{24} \\

a_{31}&a_{32}&a_{33}&a_{34} \\

a_{41}&a_{42}&a_{43}&a_{44}

end{Bmatrix}$

Given the model, several interestingquestions about weather patterns over time can be asked (and answered). We canask the question: what is the probability (according to the given model) thatthe weather for the next $k$ days willbe? Another interesting question we can ask: given that the model is in a knownstate, what is the expected number of consecutive days to stay in that state?Let us define the observation sequence $O$ as $O = left \{ s_{1}, s_{2}, s_{3}, ... , s_{k} right \}$, and the probability of the observation sequence $O$ given the model is defined as $p(O|model)$.

Also, let the expected number of consecutive days to stayin state $i$ be $E_{i}$. Assume that the initial state probabilities $p[s_{1} = i] = 1, 1 leq i leq N$. Both $p(O|model)$ and $E_{i}$ are real numbers.

Line $1$~$4$ for the state transition probabilities. Line $5$ for the observation sequence $O_{1}$, and line $6$ for the observation sequence $O_{2}$. Line $7$ and line $8$ for the states of interest to find the expected number of consecutive days to stay in these states.

Line $1$: $a_{11} a_{12} a_{13} a_{14}$

Line $2$: $a_{21} a_{22} a_{23} a_{34}$

Line $3$: $a_{31} a_{32} a_{33} a_{34}$

Line $4$: $a_{41} a_{42} a_{43} a_{44}$

Line $5$: $s_{1} s_{2} s_{3} ... s_{k}$

Line $6$: $s_{1} s_{2} s_{3} ... s_{l}$

Line $7$: $i$

Line $8$: $j$

Line $1$ and line $2$ are used to show the probabilities of the observation sequences $O_{1}$ and $O_{2}$ respectively.

Line $3$ and line $4$ are for the expected number of consecutive days to stay in states $i$ and $j$ respectively.

Line $1$: $p[O_{1} | model]$

Line $2$: $p[O_{2} | model]$

Line $3$: $E_{i}$

Line $4$: $E_{j}$

Please be reminded that the floating number should accurate to $10^{-8}$.

0.4 0.3 0.2 0.1

0.3 0.3 0.3 0.1

0.1 0.1 0.6 0.2

0.1 0.2 0.2 0.5

4 4 3 2 2 1 1 3 3

2 1 1 1 3 3 4

3

4

0.00115200

2.50000000

2.00000000

# 题解

给你一个状态转移的概率矩阵，有四组询问，前两组询问按照序列顺序出现的概率；后两种询问指定状态连续出现的期望天数

显然前者就是按照顺序求一下概率的乘积

后者可以很容易推出是等比数列求和

难点在于输入和读题

# 代码

#include <cstdio> #include <iomanip> #include <iostream> #include <sstream> #include <string> using namespace std; double p[4][4]; int O[105]; double calc1() { string s; getline(cin, s); stringstream ss(s); int lst = -1, ths = -1; double ans = 1.0; while (ss >> ths) { if (lst != -1) ans *= p[lst - 1][ths - 1]; lst = ths; } return ans; } double calc2() { int t; cin >> t; double pp = p[t - 1][t - 1]; return 1.0 + pp / (1 - pp); } int main() { //cin.tie(0); //cin.sync_with_stdio(false); for (int i = 0; i < 4; ++i) for (int j = 0; j < 4; ++j) cin >> p[i][j]; getchar(); cout << fixed << setprecision(8) << calc1() << endl; cout << fixed << setprecision(8) << calc1() << endl; cout << fixed << setprecision(8) << calc2() << endl; cout << fixed << setprecision(8) << calc2() << endl; return 0; }