Consider a maze mapped to a matrix with an upper left corner at coordinates (row, column) = (0, 0). You can only move either towards right or down from a cell. You must determine the number of distinct paths through the maze. You will always start at a position (0, 0), the top left, and end up at (n-1, m-1), the bottom right. As an example, consider the following diagram where '1' indicates an open cell and '0' indicates blocked. You can only travel through open cells, so no path can go through the cell at (0, 2). There are two distinct paths to the goal.

There are two possible paths from the cell (0, 0) to cell (1, 3) in this matrix. Complete the function numberOfPaths. The function must return the number of paths through the matrix, modulo (10^9 + 7).

**Example One**

Input:

3

4

1 1 1 1

1 1 1 1

1 1 1 1

Output: 10

There are 10 possible paths from cell (0, 0) to cell (2, 3).

**Example Two**

Input:

2

2

1 1

0 1

Output: 1

There is 1 possible path from the cell (0, 0) to cell (1, 1).

**Notes**

Input Parameters: The function contains a single argument, a two-dimensional integer array called matrix.

Output: Return an integer, the number of paths to reach from (0, 0) to (n-1, m-1).

**Constraints:**

● 1

● Each cell, matrix[i][j], contains 1, indicating it is accessible or 0, indicating it is not accessible, where 0

We provided 2 solutions.

● We have a matrix having n rows and m columns where each cell, matrix[A][B], denotes whether it is accessible or not.

● We will use recursion to solve this problem.

● We know that the number of ways to reach a cell is the sum of the number of ways to reach cell just to the left of it and the number of ways to reach cell just above it, that is for cell matrix[i][j], the number of ways to reach that cell is the number of ways to reach cell matrix[i-1][j] + number of ways to reach cell matrix[i][j-1].

● This is because we can only go in the right or downward direction from any cell.

● Now, we start from cell and perform recursion that is number of ways to reach cell (n-1, m-1) which is recur(matrix, n-1, m-1) = recur(matrix, n-2, m-1) + recur(matrix, n-1, m-2).

● For each cell we will check if it is accessible or not. If it is not we simply return 0, as there is no way to reach it.

● The base condition would be that for cell (0,0), that is the starting cell, if it is accessible then the number of ways to reach it is 1.

**Time Complexity:**

O(2^(rows+columns)).

In each recursion step, we will either reduce n by 1 or m by 1. Hence, the recurrence relation will be:

T(n + m) = T(n - 1 + m) + T(n + m - 1) + O(1)

T(n + m) = 2 * T(n - 1 + m) + O(1)

Which is O(2^(n+m)).

**Auxiliary Space Used:**

O(rows+columns). We do not create any auxiliary array for the solution. We use recursion which uses O(rows+columns) stack space as the maximum steps can be rows+columns at a time.

**Space Complexity:**

O(rows*columns). Input takes O(rows*columns) space, the auxiliary space used is O(rows+columns), and the output takes O(1). Summing these up we get O(rows*columns).

● We have a matrix having n rows and m columns where each cell, matrix[A][B], denotes whether it is accessible or not.

● As we can see from the above recursive solutions (brute_force_solution) is exponential. We are visiting multiple states again and again and then recursing for them, which we can surely avoid.

● With the same idea of avoiding recursion again and again for states that have already been visited, we store them in a dp array so that they can be accessed as and when needed.

● This approach is known as Dynamic Programming.

● We know that for any cell (i, j) the number of paths to reach it would be the number of paths to reach cell(i-1, j) + number of paths to reach cell (i, j-1).

● For any accessible cell matrix[i][j], we maintain the count of number of paths to reach this cell in a separate two-dimensional array dp[i][j], where dp[i][j] = dp[i-1][j] + dp[i][j-1], given that cell (i, j) is accessible.

● We return the value of dp[i][j] as the answer.

**Time Complexity:**

O(n*m) considering the number of rows are n and columns are m.

We traverse the entire array matrix once and simultaneously traverse the dp array to keep the number of ways to reach that cell from cell (0,0). So we travel both arrays at most 1 time and hence the time complexity is O(n*m).

**Auxiliary Space Used:**

O(n*m) considering the number of rows are n and columns are m. We create a two-dimensional array dp of size n*m to store the number of paths from cell (0,0) to the current cell. Hence, auxiliary space used is O(n*m).

**Space Complexity:**

O(n*m) considering the number of rows are n and columns are m. Input complexity is O(n*m), auxiliary space used is O(n*m), and output space complexity is O(1), hence total complexity will be O(n*m).

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