How to convert the following dictionary into the expected outcome matrix in python? dictionary = { 0:[0,1], 1:[2,3], 2:[0,1], 3:[1,2] } nodes = 4 expected_outcome_matrix = np.array([ [1, 1, 0, 0], [0, 0, 1, 1], [1, 1, 0, 0], [0, 1, 1, 0]])
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How to convert the following dictionary into the expected outcome matrix in python?
dictionary = {
0:[0,1],
1:[2,3],
2:[0,1],
3:[1,2]
}
nodes = 4
expected_outcome_matrix = np.array([ [1, 1, 0, 0],
[0, 0, 1, 1],
[1, 1, 0, 0],
[0, 1, 1, 0]])
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- Please debug the following trenchTranveral javascript function. The function should return true if the node's value is less than -5 and false if equal to or greater than -5. function findNeighbors(node, matrix) { const neighbors = []; const [row, col] = node; const currNum = matrix[row][col]; // Check top if (matrix[row - 1] && Math.abs(matrix[row - 1][col] < - 5)) { neighbors.push([row - 1, col]); } // Check bottom if (matrix[row + 1] && Math.abs(matrix[row + 1][col] < - 5)) { neighbors.push([row + 1, col]); } // Check left if (matrix[row][col - 1] && Math.abs(matrix[row][col - 1] < - 5)) { neighbors.push([row, col - 1]); } // Check right if (matrix[row][col + 1] && Math.abs(matrix[row][col + 1] < - 5)) { neighbors.push([row, col + 1]); } // Return neighbors return neighbors; // Only consider N, S, E, W nodes } function trenchTraversal(node, matrix, visited) { const [row, col] = node; let stack = [node] visited.add(node.toString())…Hypertune these parameters by using grid search: stack_rgs = StackingRegressor(estimators = [ ('ridge_regression', Ridge(random_state=42)), ('sgdregressor', SGDRegressor(random_state=42)), ('svr', SVR(kernel='rbf'))Suppose that queue is implemented as an array with the special reserved slot, Suppose that the size of the array implementing queue is 100. Also, suppose that the value of queueFront is 74 and the value of queueRear is 99. a. What are the values of queueFront and queueRear after adding an element to queue?b. What are the values of queueFront and queueRear after removing an element from queue? Also, what is the position of the removed queue element?
- Python program for addition of 2 matrix's class Matrix: def __init__(self, List); def display(self): print(self.List) for i in range(len(self.List)) Temp.List.append(self.List[i][j]+M.List[i][j]) return Temp Matrix1=Matrix([[1,2],[3,4]]) Matrix2=Matrix([[4,5],[6,7]]) Matrix3=Matrix([]) Matrix3=Matrix1+Matrix2 print("The addition of Matrix: ") Matrix3.display()class Matrix: def __init__(self, List) print(self.List) def __add__(self,M): for j in range(len(self.List[0])): Matrix1=Matrix([[12],[3,4]])Matrix2=Matrix([[4,5],[6,7]])Matrix3=Matrix([])Matrix3=Matrix1+Matrix2print("The addition of Matrix: ")Matrix3.display()The pseudo-code to create the matrix:Procedure AdjacencyMatrix(N): //N represents the number of nodesMatrix[N][N]for i from 1 to N for j from 1 to N Take input -> Matrix[i][j] endforendfor Make Java code implemented using above pseudo-code:Write in C Language Description A sparse matrix is a matrix in which most of the elements are zero. Representing a sparse matrix by a 2D array leads to wastage of lots of memory as zeroes in the matrix are of no use in most of the cases. So, we can compress the sparse matrix by only store non-zero elements. This means storing non-zero elements with triples- (Row, Column, value). 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 6 0 0 0 0 9 0 0 0 0 0 0 0 12 0 Here is a 5X6 matrix, there are 4 element non-zero, we using three number represent it 5 6 4 After the three number are (Row, Column, value): row col val 1 1 3 2 3 6 3 2 9 4 4 12 Write a program to read the compressed matrix and print out the original matrix. Input First line consists 3 integers. First and second integers shows the dimensions of original matrix. Third integer k shows number of non-zero elements. Followed k lines are content of the sparse matrix. Each line consists 3 integers, row number, column number, and data. Output Output…
- Write in C Language Description A sparse matrix is a matrix in which most of the elements are zero. Representing a sparse matrix by a 2D array leads to wastage of lots of memory as zeroes in the matrix are of no use in most of the cases. So, we can compress the sparse matrix by only store non-zero elements. This means storing non-zero elements with triples- (Row, Column, value). 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 6 0 0 0 0 9 0 0 0 0 0 0 0 12 0 Here is a 5X6 matrix, there are 4 element non-zero, we using three number represent it 5 6 4 After the three number are (Row, Column, value): row col val 1 1 3 2 3 6 3 2 9 4 4 12 Write a program to read the compressed matrix and print out the original matrix. Input First line consists 3 integers. First and second integers shows the dimensions of original matrix. Third integer k shows number of non-zero elements. Followed k lines are content of the sparse matrix. Each line consists 3 integers, row number, column number, and data. Output Output…Consider the following code: typedef unordered_map<int,int> Table; bool matches(int k, int v, const Table& table) { auto pos = table.find(k); return (pos != table.end()) && (*pos == v); } int countMatches (const vector<int>& keys, list<int> values, const Table& table) { auto p1 = keys.begin(); auto p2 = values.begin(); int cnt = 0; while (p1 != keys.end() && p2 != values.end()) { if (matches(*p1, *p2, table)) ++cnt; ++p1; ++p2; } return cnt; } Let M, N, and K denote the size of keys, values, and table, respectively. What is the average case complexity of matches? O( ) What is the average case complexity of countMatches? O( )How to convert the following dictionary into expected outcome list in python? dictionary = { 0:[2,3], 1:[3], 2:[0], 3:[1] } nodes = 4 expected_outcome_list = [(0,2), (0,3), (1,3), (2,0), (3,1)]
- javascript only: You have been assigned to work with an undersea explorer who is attempting to identify and map undersea trenches. The explorer has provided you with several data sets. Depending on the scan, the provided matrix may be larger or smaller, but it will always be rectangular. Your task is to determine if a given data set contains a trench by comparing each node and their neighbors and determining if there is a pattern that matches the defined properties of a trench. Neighbors are considered to be nodes that are directly above, below, or to the side. No diagonals! A trench has the following three properties: It has a length of three or more nodes that are neighbors. Each node in the trench must be deeper than -5. Trenches may not branch into (any form of) a "T" shape. A node with more than two neighbors will result in branching "T" shape. // Example 1 sonar = [ [-5,-5,-5,-5,-5], [-5,-8,-8,-9,-7], [-5,-5,-5,-5,-8], [-5,-5,-5,-5,-5] ]Given a matrix of m x n elements (m rows, n columns),return all elements of the matrix in spiral order.For example,Given the following matrix:[ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 7, 8, 9 ]]You should return [1,2,3,6,9,8,7,4,5].""" def spiral_traversal(matrix): res = [] if len(matrix) == 0: return res row_begin = 0 row_end = len(matrix) - 1 col_begin = 0 col_end = len(matrix[0]) - 1 while row_begin <= row_end and col_begin <= col_end: for i in range(col_begin, col_end+1): res.append(matrix[row_begin][i]) row_begin += 1 for i in range(row_begin, row_end+1): res.append(matrix[i][col_end]) col_end -= 1 if row_begin <= row_end: for i in range(col_end, col_begin-1, -1): res.append(matrix[row_end][i]) row_end -= 1 if col_begin <= col_end: for i in range(row_end, row_begin-1, -1): res.append(matrix[i][col_begin]) col_begin += 1python # Initialize a 6x6 matrix with all elements as 0 matrix = [[0 for _ in range(6)] for _ in range(6)] # Fill in the matrix for i in range(6): # Iterate over rows for j in range(6): # Iterate over columns if i > j: matrix[i][j] = 3*j - 4*i else: matrix[i][j] = 0 # Print the matrix for row in matrix: print(row)