Answer:
B C++
Explanation:
The two most common languages for game designers to learn are C++ and Java, although other languages are popular (such as C# for Unity). Another type of programming you may hear referred to is scripting, but that essentially comes down to a type of systems programming.
The main purpose behind graphic designing is to communicate, The different colors, shapes and patterns used in graphics aim to deliver a meaningful message, which sometimes is hidden. Back in the time, graphic designs usually portrayed what was going on at that time, for instance wars, with larger icons and catchy slogans. Over the time, the graphic designs have evolved where psychedelic, geometrical and monochromatic designs have gained more popularity, with different typographic styles to follow. Moreover, since the world has been taken over by technology in a storm, we get to see some drastic changes in the photography styles, use of color balances with minimal designs and subtle tones.
Answer:
def brute_force(array1, array2):
for item in array1:
for element in array 2:
if element == item:
print(f"{element} and {item} are a match")
Explanation:
A brute-force algorithm is a direct-to-solution algorithm that searches and compares variables. It is like trying to unlock a safe but not knowing its four-digit combination, brute-force starts from 0000 through 9999 to get a match.
The python program implements the algorithm using two nested for loops. The first loop iterates over array1 while the second, over array2. For every item in the first array, the program loops through the length of the second array. For every match, the items are printed on the screen.
K-means can be used for hierarchical clustering by creating a hierarchical tree structure. This is done by setting the number of clusters to be created, and then running the k-means clustering algorithm for each level of the tree. For each level, the clusters created are then combined to form the next level of the tree. This process is repeated until the desired number of clusters has been created.
<h3>The Use of K-Means Clustering for Hierarchical Clustering</h3>
K-means clustering is a popular technique used in machine learning and data mining for partitioning data into clusters. It is a flat clustering algorithm, in which data points are grouped according to their similarity. While k-means clustering is suitable for partitioning data into a fixed number of clusters, it can also be used for hierarchical clustering. Hierarchical clustering is a clustering technique that creates a hierarchical tree structure, where each level of the tree is made up of clusters created by the k-means clustering algorithm.
The process of creating a hierarchical tree structure using k-means clustering is fairly straightforward. First, the number of clusters to be created is set, and then the k-means clustering algorithm is run for each level of the tree. For each level, the clusters created are then combined to form the next level of the tree until the desired number of clusters has been created. This process ensures that the clusters created are meaningful and have similar characteristics.
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