Remember Fibonacci Sequence?

How to make use of diagonalization in Fibonacci Sequence?

Let’s have a glance at transformation matrix!

• e.g. f(n+1)=h*f(n)+k*f(n-1)
• A: transition matrix from f(n+1) to (n)
• D: similar matrix formed with eigenvalues of A
• P: eigenvectors corresponding to eigenvalues
• P-¹: inverse matrix of P
• detP: determinant of matrix…

Remember binary search tree?

How to prevent from building skewed trees?

Let’s have a glance at Red-Black Tree and AVL Tree!

• Balance Factor: the distance between the height of the children.
• Each node has a Balance Factor in {-1,0,1}.
• N: stands for the new insertion.

Still remember binary search tree? (1 key, 2 degree)

How to decrease the depth of a such tree?

What if each node contains not unary key?

Let’s have a glance at M-Way Search Trees in balanced!

• Node: contains multi-records.
• Record: made of a key and data.
• Degree: m.

How to observe changing state in server?

Let’s have a glance at WebSocket!

• Client: send handshake to server.
• Server: response acknowledgement to client.
• Connection setup, start messaging in bi-directional until disconnected.

Take following as Demo via Chatting App.

At first, send a request to server for handshake.
Then, server response a acknowledgement.
That’s all, start bi-directional messaging.

• Client emit echo message.
• Client emit broadcast message.
• Client listen to message emit from server.
• Server emit broadcast message to every client.
• Server emit echo message to each client.
• Server emit time message to every client.

How to observe changing state in server?

Let’s have a glance at Comet!

• Client: send request to server then blocking until response or timeout.
• Server: handle changing data and response to client in time.
• Disconnect per response or timeout.

Take following as Demo via Human App.

At first, send a request to server for making a human.
Then, long polling human state handled by server.

Wanna keep learning? How do humans keep learning?

Let’s use a model illustrate learning, from Reinforcement Learning.

• X: as reason space, {x|f(x)=y}.
• Y: as result space, {y|f(x)=y}.
• P(x|y): possibility of given y then x.
• P(y|x)=100% : observation of given x then y.
• Knowledge Repo: where stores conclusion of extracted P(x|y).
• Experience Repo: where stores observation of P(y|x)=100%

Followed by actions and mindcept.

• Imitate, Copy, What, How
• Compare, Link, Imagine, Who, Which, When, Where
• Conclude, Generalize, Why
• Create, Apply, Invent, Wow

To be continued.

Any other thoughts? Just response directly.

What is Message Queue?

Let’s have a glance at Message Queue!

• Producer: the role publishing messages to channels
• Consumer: the role consuming messages from channels
• Borker: playing as a media decoupling producers and consumers
• Channel: playing as a message queue featured with FIFO

Take following as Demo via Redis.

At first, launch a broker.
Here are 2 channels, named “naruto” and “kuroku”.

Second, the 2 producers publish messages to each channel.
Finally, the 2 subscribers consume messages from each channel.