BMAN73701 Week 3 Numerical Analysis Quiz

False (B)

Error: operands could not be broadcast together

<p>sum = Aggregation operation mean = Aggregation operation reshape = Array manipulation broadcasting = Enables operations on different shaped arrays</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>4</p> Signup and view all the answers

<p>Reshape = Alters the dimensions of an array Pairwise product = Multiplies corresponding elements from two arrays Sum = Calculates the aggregate of values Total = Final result of all pairwise products</p> Signup and view all the answers

<p>The products of x and y elements (B)</p> Signup and view all the answers

<p>2, 3, 4, 5</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>x[[0, 1]] (A), x[range(2)] (C)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>array([[3, 2], [4, 5]])</p> Signup and view all the answers

<p>len(x) - 1</p> Signup and view all the answers

<p>x[1:, :1] (B)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Selects the first two columns for all three rows.</p> Signup and view all the answers

<p>x[:, 1:] = (3, 2) x[0:2, :] = (2, 3) x[:1, :] = (1, 3) x[-1, :] = (3,)</p> Signup and view all the answers

<p>Random number generation (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>np.random.rand</p> Signup and view all the answers

<p>scipy.optimize</p> Signup and view all the answers

<p>Both np.mean and np.average (A)</p> Signup and view all the answers

<p>It initializes the random number generator with a seed value.</p> Signup and view all the answers

<p>scipy.optimize = Numerical optimization scipy.linalg = Linear algebra operations scipy.stats = Statistical tests numpy.random = Random number functions</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>The minimum along the rows. (D)</p> Signup and view all the answers

<p>The maximum Mean Squared Error (MSE)</p> Signup and view all the answers

<p>columns</p> Signup and view all the answers

<p>np.min(A) = Returns the minimum value of the array np.min(A, axis=0) = Returns minimum along the row (per column) np.min(A, axis=1) = Returns minimum along the columns (per row) np.max(np.mean((P-obs)**2, axis = 1)) = Calculates maximum Mean Squared Error</p> Signup and view all the answers

<p>P (D)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>The difference between predicted values and actual observations</p> Signup and view all the answers

<p>A.min() (C)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>false</p> Signup and view all the answers

<p>0</p> Signup and view all the answers

<p>np.all() = Checks if all elements are true np.any() = Checks if any element is true np.logical_and() = Computes the logical AND operation np.logical_not() = Inverts the truth value of elements</p> Signup and view all the answers

<p>1 value per column (C)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>np.any(A &lt; 0)</p> Signup and view all the answers

Calculating the sum of all possible products obtained by multiplying corresponding elements from each of the two vectors.

The product of corresponding elements from the input vectors.

Adding up the results of all pairwise products of the elements across the two vectors.

A computational approach that utilizes optimized array operations for efficiency; avoiding explicit loops (iterating through entries one by one).

A traditional programming concept involving loops inside loops to process datasets element by element.

Changing the dimensional structure of a vector or array to fit the requirements.

NumPy function used to sum the products of corresponding elements.

A technique in NumPy that allows array operations between arrays of different shapes.

Extracting portions of an array using specific indexes (rows and columns).

Selecting a range of elements in an array with colon notation. x[start:end:step]

The first index of the array or the first element in a list/array.

Refers to the last element of an array/list.

Selecting elements based on whether a condition is True or False.

Accessing elements at a specified row and column position within a 2-D array.

Select all rows from a 2D array, but only the second column (index 1).

Select the last row (index -1) and all columns from a 2D array.

A mechanism in NumPy that allows mathematical operations between arrays of different shapes, providing a more concise and efficient way to manipulate data.

When NumPy arrays have shapes that don't allow for broadcasting, an error occurs because operations can't be performed element-wise in a consistent manner.

Process of changing the dimensions (rows, columns) of a NumPy array without changing the total number of elements.

Multiplication where the corresponding elements of each array are multiplied together.

NumPy functions like sum(), min(), max(), mean() that compute a single output value from an array or array subset.

Finds the minimum value in a NumPy array A.

Finds the minimum value along each column of a NumPy array A.

Finds the minimum value along each row of a NumPy array A.

Calculates the maximum Mean Squared Error (MSE) from predictions (P) and observations (obs).

The formula for finding maxMSE is maxMSE = np.max(np.mean((P-obs)**2, axis=1))

A matrix where each row represents the predictions from a machine learning method across all observations, and each column represents a single observation.

A vector containing the actual observed values for each observation.

A measure of the average squared difference between predicted and actual values.

Functions that operate on NumPy arrays, providing specific functionalities. They can be accessed using the dot notation (.) on the array object.

Some NumPy methods directly change the contents of the original array without creating a copy.

Arrays holding only True or False values, representing conditions on data.

Checks for the presence of at least one negative value in the array 'A'.

Checks if all values in the array 'A' are negative.

Checks for columns in the array 'A' where all values are negative.

Checks for rows in the array 'A' where at least one value is negative.

A parameter used in some NumPy functions to specify the direction of operation, such as along rows (axis=0) or columns (axis=1).

A submodule of NumPy that provides functions for generating random numbers, distributions, and permutations.

Sets the initial state of the random number generator. When the seed is the same, you'll get the same sequence of random numbers.

A library built on top of NumPy designed for numerical analysis and scientific computing. It provides advanced functions for optimization, linear algebra, and statistics.

A submodule within SciPy that offers functions for solving optimization problems. It handles tasks like maximizing/minimizing functions, both linear and non-linear.

A submodule within SciPy for faster and more robust linear algebra operations compared to NumPy's np.linalg.

A SciPy submodule focused on statistical functions, distributions, hypothesis testing, and calculations like trimmed and geometric means.

Functions like sum, min, max, mean, all, any that condense data into a single value, summarizing its key characteristics.