The Matrix

A passive Christian hero's story against the background of baroque MTV, Matrix is ​​a clear combination of technical magic and contextual excellence, and should be a benchmark for all science fiction films.

Hollywood encountered several problems in combining formats and problems in the field of science fiction. There are many visually stunning works, but no one cares about heroes. However, some people have recalled interests and wisdom, but no one can influence these special effects. In The Matrix, both elements are perfectly synchronized. We wish not only to support the victories of the heroes but also that they can fight the opposition. Because Neo is avoiding those bullets in a tedious way, we are not just wonderful, we are anxious.

However, Matrix somewhat reduces the type of movie, there is no loophole in other kinds of movies. The script written by Wakovsky brothers is very smart, but careless. Kung Fu sequence is shot cleverly - even Bruce Lee will be proud of it. The pictures are wonderful. (If you need to cut all the frames on the reel, expand it and print it out, each frame will be self-contained.) And do you do it? Maybe it is not the best Keanu Reeves, but I can say that it is an actor with a box office appeal, but I draw a relaxing and vulnerable hero Ne as T like Reeves can do. But please think about it. If you cause problems around someone around Lawrence Fishbourne, it will undoubtedly confuse the actor because of its poor performance. Like Morpheus, Fishburn is simply evil! His leadership role in higher education has no such quiet atmosphere as Mr. Freeborn. His role is scary, but always consisted of Morpheus and gives extra death (he likes neo to make a mistake).

People will say how well they want in the matrix, but the final result is this: In the end there is a philosophical film running through this generation. My generation Wakovsky brothers may write P. S for a while. What is a matrix? Something closer to reality than you think

The covariance matrix of sample X can be written as the product of the transpose matrix X and X itself. According to the Rayleigh quotient, the maximum variation of our sample lies on the eigenvectors of the matrix and is consistent with the largest eigenvalue. Therefore, the principal component held from the data should be only the feature vector corresponding to the first k largest eigenvalues ​​of the matrix. Digestion is easy for the next step. Multiply the data matrix X by these elements and project the data onto the orthogonal basis of the selected element. If the number of components is less than the initial spatial dimension, remember that applying this transformation will result in loss of information.

A well-known matrix decomposition method is singular value decomposition (SVD). At higher levels, SVD is an algorithm that decomposes Matrix A into the lowest level (ie, smaller / simpler) of the original matrix A. Mathematically, we break up A into two unitary matrices and diagonal matrices. Where A is the input data matrix (user evaluation), U is the left singular vector (user's "feature" matrix) and Sum is a singular value pair. Angle matrix (basically the weight / intensity of each concept) V ^ T is a right singular vector ("feature" matrix of the movie). U and V ^ T are column orthogonal and represent different things. U indicates how much the user likes each function, and V ^ T indicates how much each function is associated with each movie.

Let A be an m × n matrix. We define U as an m × m matrix, D as an m × n matrix, and V as an n × n matrix. Each of these matrices is defined as having a special structure. Both matrices U and V are defined as orthogonal matrices. The matrix D is defined as a diagonal matrix. Please note that D is not necessarily a square. The elements along the diagonal of D are called singular values ​​of matrix A. The U column is called the left singular vector. Right singular vector