The MongoDB shell launches and the prompt is ready to accept your commands. Instead of just typing mongo, you can additionally define a specific host and port by typing: mongo -host localhost -port 27017. With the MongoDB shell, you can now create a database, add collections or manage individual documents. In this Linux/Unix command line cheat sheet, you will learn: Basic Linux commands File Permission commands Environment Variables command User management commands of linux Networking command Process co. It can be really frustrating to have a reverse shell (or think you have one), run a command and not see anything come back and not even know if it ran or not. Sometimes, there is a simple solution like adding a “2&1” at the end of your command. There are many other things you can do to clean up your shell and tty. MongoDb Cheat Sheet MongoDB Cheat Sheet Db Info Commands Start a mongo shell to remote Db Commands Users Create User Backup / Restore Backup Restore with mongorestore Find Querying References Db Info Commands Start a mongo shell to remote Db mongo -host Test01 -port -username adminuser -password -authenticationDatabase admin.
NumPy Matrix and Linear Algebra
What's difference between numpy dot() and inner()?
Let's look into 2D array as an example:
$begin{bmatrix} 1 & 2 3 & 4 end{bmatrix}$$begin{bmatrix} 11 & 12 13 & 14 end{bmatrix}$
With dot():
$begin{bmatrix} 1*11+2*13 & 1*12+2*14 3*11+4*13 & 3*12+4*14 end{bmatrix}$=$begin{bmatrix} 37 & 40 85 & 92 end{bmatrix}$With inner():
$begin{bmatrix} 1*11+2*12 & 1*13+2*14 3*11+4*12 & 3*13+4*14 end{bmatrix}$=$begin{bmatrix} 35 & 41 81 & 95 end{bmatrix}$The chapters on NumPy have been using arrays (NumPy Array Basics A and NumPy Array Basics B). However, for certain areas such as linear algebra, we may instead want to use matrix.
We may also take the Matlab style by giving a string rather than a list:
Vectors are handled as matrices with one row or one column: Download xcode 12.
Here is an example for matrix and vector multiplication:
For vectors, indexing requires two indices: Parallel desktop 13 activation key.
Though np.matrix takes a real matrix form and look pleasing, usually, for most of the cases, arrays are good enough.
Note that the rank of the array is not the rank of the matrix in linear algebra (dimension of the column space) but the number of subscripts it takes!
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Scalars have rank 0:
NumPy supports arrays of any dimension such as rank 3 (2x2x2):
Another example:
$$A=begin{bmatrix} 1 & 2 & 3 4 & 5 & 6 end{bmatrix}, B=begin{bmatrix} 7 & 8 & 9 end{bmatrix}$$Ms office mac 2019 download. Now we want to solve Ax = b:
The eig returns two tuples: the first one is the eigen values and the second one is a matrix whose columns are the two eigen vectors.
We can unpack the tuples:
We want to solve $int_0^{3}x^4dx = frac{243}{4}$:
The returned tuple indicates (ans, error estimate).
We can get the same answer if we use lambda instead:
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