Install yii Framework via Composer



jika belum terinstall Composer , silahkan install composer dengan command berikut. berlaku untuk MacOS dan Linux.

curl -sS https://getcomposer.org/installer | php 
mv composer.phar /usr/local/bin/composer 

anda bisa mengupdate composer dengan command berikut

composer self-update

jika sudah terinstall composer, anda bisa install yii framework dengan command berikut

composer global require "fxp/composer-asset-plugin:~1.1.1"

contoh create yii framework dengan nama folder coba

composer create-project --prefer-dist yiisoft/yii2-app-basic basic

Note : Dalam proses create ini, kita akan diminta untuk mengisikan TOKEN, jadi copy saya URL yang ditujukan saat diminta mengisi token, seperti berikut :

https://github.com/settings/tokens/new?scopes=repo&description=Composer+on+fairuz-Inspiron-N4050+2016-05-29+1629

itu adalah URL yang ada pada laptop saya.. copykan URL tersebut pada browser, maka akan muncul halaman berikut.




Klik Generate Token




Copykan Token yang sudah digenerate ke command line,. kemudian enter, dan tunggu sampai proses selesai.

Setelah berhasil instalasi, dan create projectnya,. silahkan cek "http://localhost/coba/web" maka akan muncul halaman "Congratulation" berikut ini..


selesai,.. mudah kan :D
selamat mencoba... 




reference
http://www.yiiframework.com/doc-2.0/guide-start-installation.html
             

              There are fundamental difference and comparisson between Linux and Windows. Linux is an example of Open Source Software Development and free Operating System (OS), and Windows is the family of operating system (OS) from Microsoft, which is the most famous OS in the world. There are basic difference and comparison between linux and windows that will always set them apart but this is not in the least, to say one is better then the other. It's just to say that they are fundamentally difference and comparison. Linux and windows have many compare and contrast, but many people looking from the view of one operating system or the other, don't quite get the difference and comparison between these two powerhouses.
            So, there are compare between Linux and Windows. First, both Linux and Windows are Operating System, but Linux has more special futures especially for college students in Informatics Engineering . Second, Linux and windows comes in many flavors. All the flavors of windows come from Microsoft, the various distributions of linux come from different companies (i.e Linspire, Red Hat, SuSE, Ubuntu, Xandros, Knoppix, Slackware, Lycoris, etc.). Third,  Like Windows, Linux provide GUI and Command Line Interface. The windows Gui is an integral component of the OS and is not replaceable. This can be a con when it comes to Windows 8' Metro, and Linux typically provides two GUIs, KDE and Gnome, But there are millions of alternatives such as LXDE, Xfce, Unity, Mate, twm, etc. Fourth,  Either Windows and Linux can recovery file. We can recovery file was file is deleted permanent or unpermanent, but maybe just 75 – 90 % file comeback with recovery process. Fifth, Windows and Linux have multimedia in many flavor.
            Then, there are contrast between Linux and Windows. First, Unlike Linux, almost all games are compatible with windows. Linux very few games available natively, some games can be played through wine, but often not all features are availabe but windows has some CPU intensive and graphics intensive are exclusive to make compatible all games. Second, Linux can be freely whereas windows can be expensive. Linux can be freely distributed, downloaded, distributed through magazines, book, etc but windows can be expensive for dekstop or home use. Third, Linux had about 60-100 virues listed till date more than 60.000 virues in windows. Fourth, Linux Supported in all platform, but windows only some platform. Windows can supported in more platform such as in PowerPC: versions 1.0 -NT 4.0; DEC Alpha: versions 1.0 – NT 4.0; MIPS R4000: versions 1.0 – NT 4.0; IA-32: versions 1.0 – 8; IA 64: version XP; x86-64: versions XP -8; ARM: version RT. Fifth, Windows has a good interface and future than linux. Linux use Gnome or KDE (Depends or distro) to default user interface but windows use Graphical (Windows Aero) to default user interface with provide many future. 
            In short, windows is friendly user with the good face and future, however linux is serverely operating system with strongly security system.


Tree Data Structure



Definition
A tree is a (possibly non-linear) data structure made up of nodes or vertices and edges without having any cycle. The tree with no nodes is called the null orempty tree. A tree that is not empty consists of a root node and potentially many levels of additional nodes that form a hierarchy.
In computer science, a tree is a widely used abstract data type (ADT) or data structure implementing this ADT that simulates a hierarchical tree structure, with a root value and subtrees of children, represented as a set of linked nodes.
A tree data structure can be defined recursively (locally) as a collection of nodes (starting at a root node), where each node is a data structure consisting of a value, together with a list of references to nodes (the "children"), with the constraints that no reference is duplicated, and none points to the root.
Alternatively, a tree can be defined abstractly as a whole (globally) as an ordered tree, with a value assigned to each node. Both these perspectives are useful: while a tree can be analyzed mathematically as a whole, when actually represented as a data structure it is usually represented and worked with separately by node (rather than as a list of nodes and an adjacency list of edges between nodes, as one may represent a digraph, for instance). For example, looking at a tree as a whole, one can talk about "the parent node" of a given node, but in general as a data structure a given node only contains the list of its children, but does not contain a reference to its parent (if any).

Terminologies Used in Trees
·         Root – The top node in a tree.
·         Parent – The converse notion of child.
·         Siblings – Nodes with the same parent.
·         Descendant – a node reachable by repeated proceeding from parent to child.
·         Ancestor – a node reachable by repeated proceeding from child to parent.
·         Leaf – a node with no children.
·         Internal node – a node with at least one child.
·         External node – a node with no children.
·         Degree – number of sub trees of a node.
·         Edge – connection between one node to another.
·         Path – a sequence of nodes and edges connecting a node with a descendant.
·         Level – The level of a node is defined by 1 + the number of connections between the node and the root.
·         Height of tree –The height of a tree is the number of edges on the longest downward path between the root and a leaf.
·         Height of node –The height of a node is the number of edges on the longest downward path between that node and a leaf.
·         Depth –The depth of a node is the number of edges from the node to the tree's root node.
·         Forest – A forest is a set of n ≥ 0 disjoint trees.

node is a structure which may contain a value or condition, or represent a separate data structure (which could be a tree of its own). Each node in a tree has zero or morechild nodes, which are below it in the tree (by convention, trees are drawn growing downwards). A node that has a child is called the child's parent node (or ancestor node, orsuperior). A node has at most one parent.
An internal node (also known as an inner nodeinode for short, or branch node) is any node of a tree that has child nodes. Similarly, an external node (also known as anouter nodeleaf node, or terminal node) is any node that does not have child nodes.
The topmost node in a tree is called the root node. Depending on definition, a tree may be required to have a root node (in which case all trees are non-empty), or may be allowed to be empty, in which case it does not necessarily have a root node. Being the topmost node, the root node will not have a parent. It is the node at which algorithms on the tree begin, since as a data structure, one can only pass from parents to children. Note that some algorithms (such as post-order depth-first search) begin at the root, but first visit leaf nodes (access the value of leaf nodes), only visit the root last (i.e., they first access the children of the root, but only access the value of the root last). All other nodes can be reached from it by following edges or links. (In the formal definition, each such path is also unique.) In diagrams, the root node is conventionally drawn at the top. In some trees, such as heaps, the root node has special properties. Every node in a tree can be seen as the root node of the subtree rooted at that node.
The height of a node is the length of the longest downward path to a leaf from that node. The height of the root is the height of the tree. The depth of a node is the length of the path to its root (i.e., its root path). This is commonly needed in the manipulation of the various self-balancing trees, AVL Trees in particular. The root node has depth zero, leaf nodes have height zero, and a tree with only a single node (hence both a root and leaf) has depth and height zero. Conventionally, an empty tree (tree with no nodes, if such are allowed) has depth and height −1.
subtree of a tree T is a tree consisting of a node in T and all of its descendants in T.[c][1] Nodes thus correspond to subtrees (each node corresponds to the subtree of itself and all its descendants) – the subtree corresponding to the root node is the entire tree, and each node is the root node of the subtree it determines; the subtree corresponding to any other node is called a proper subtree (by analogy to a proper subset).



Binary Trees
The simplest form of tree is a binary tree. A binary tree consists of
  1. node (called the root node) and
  2. left and right sub-trees.
    Both the sub-trees are themselves binary trees.
You now have a recursively defined data structure. (It is also possible to define a list recursively: can you see how?)

A binary tree

The nodes at the lowest levels of the tree (the ones with no sub-trees) are called leaves.
In an ordered binary tree,
  1. the keys of all the nodes in the left sub-tree are less than that of the root,
  2. the keys of all the nodes in the right sub-tree are greater than that of the root,
  3. the left and right sub-trees are themselves ordered binary trees.
For example, if you construct a binary tree to store numeric values such that each left sub-tree contains larger values and each right sub-tree contains smaller values then it is easy to search the tree for any particular value. The algorithm is simply a tree search equivalent of a binary search:

start at the root
REPEAT until you reach a terminal node
 IF value at the node = search value
                             THEN found
 IF value at node < search value
        THEN move to left descendant
        ELSE move to right descendant
END REPEAT

Conclusion

A tree is a data structure made up of nodes or vertices and edges without having any cycle. And example for The simplest form of tree is a binary tree.

References

http://www.i-programmer.info/babbages-bag/477-trees.html
























We might forgive a high-tech billionaire for making premature claims about the capabilities of autonomous vehicles. After all, keeping one between the relevant navigational beacons is piece of cake for special purpose algorithms running on digital hardware. The trouble is that those last few percentage points of occupant or bystander safety seem to get them every time. Many technology fans, including DARPA, think that brain-like neuromorphic chips that bleep digital spikes at each other could provide the answer — if only someone knew what exactly should generate the spikes they use. The latest brainstorm, before turning over the keys of life to these chips, is to put them into autonomous quadcopters and see what happens.
Obviously these neuromorphic chips are not just stuck onto the drones, but rather integrated somehow within the sensor-to-motor pipeline. Generally this means that the analog data from miscellaneous ultrasonic and optical sensors gets triaged in some fashion into neuro-digital spikes, which are propagated to higher level neurons
In the absence of the full physical vetting process (i.e. biology) used by real neurons, some kind of adaption algorithm is expediently imposed on individual neurons of the network. For some neurons, it could be as simple as something like this: Detection of big or noisy thing = spike fast, plus add some little twist to the network in lieu of real growing connections.

This extra little step, a bit of neuromorphic lagniappe if you will, could be as simple as say, “increase connections with those particular neighbors that are spiking slow.” As long as the algorithms conform in some way to the real world expectation that the power (or whatever other cost function exists in neuromorph land) for the individual spikes draws from a finite pool of energy, then the hope is that network activity won’t get out of hand.
The quadcopter itself is custom built by AeroVironment, a unique company founded by Paul MacCready, with some funding from DARPA’s neuromorphic SyNAPSE project. Incidentally, the late MacCready became known internationally as the father of human-powered flight after building both the Gossamer Condor and Gossamer Albatross pedal craft. Not long ago, Bryan Adams, who first piloted the Albatross in a historic flight across the English Channel gave us a few insightful comments in one of our posts about human-powered flight. With its latest creation, AeroVironment has basically built a flying chip that continues its tradition of extreme lightness. At only 93 grams, 18 of those precious grams are the neuromorphic control chip.
The chip itself (shown in the center of the drone at the top of the story) is the product of an even more historic and iconic company, HRL Laboratories. Today Howard Hughes Research Labs is jointly owned by GM and Boeing. Having grown bored with trying to get huge wooden planes airborne (the Spruce Goose) or getting Ruby lasers to lase, HRL now dabbles in AI research — and apparently, neuromorphics. On a budget of just 50 milliwatts of power, its 576-neuron chip can recognize its surroundings and report to the drone whether or not they are in a familiar environment. As more details about this chip emerge, it would be interesting to compare its neural powers with more vision-specific drone control chips like for example, the neuromorphic bug eyecam.
 
A neuromorphic chip. (Not the one that powers the quadcopter.)
Not surprisingly, a few neuroscientists are anxious to get their hands on this chip. One thing they might like to try is to see if its network develops prosopagnosia (selective inability to recognize faces) when they cut its power down to 25 milliwatts. An even more telling sign that the chip is mimicking real brain function would be if the researchers could impart an intuitive emotional feel for different rooms or locations to the drone. The potential ability of the drone to feel different levels of safety or affinity for different places might be reflected in detection of the Capgras delusion when its emotion neurons were deactivated. In humans, this particular syndrome leaves the ability to detect faces intact, but impairs their ability to assign emotional content to the faces of those close to them. The curious result is that they claim the person is an impostor, a doppelganger of the real person who must be somewhere else.
One trend to observe in the control systems now being fleshed out for autonomous vehicles is the blooming demand for real time processors with advanced interrupt handling. As algorithms or threads compete for compute cycles and priority level either within a single processor or across several for control of the vehicle, conflicts will increasingly loom. At the extreme, the bulk of the effort in traditional computing systems then just becomes suspending threads and writing their incidentals to memory while higher priority events attempt to hold forth. In many ways, the neuro-digital spike itself can be thought of as an interrupt — all interrupt computing if you will. When the conversion from spike to bits, or from bits to spike becomes sufficiently muddled, then algorithm-free spikes must be both everything and nothing. In other words, not only are spikes a Morse code with that is all dots and no dashes, it is also a Morse code entirely devoid of any symbolic translation.
Practical neural chips are not quite ready to shed the algorithm entirely. The most important algorithm that awaits is probably not one that is network-wide, but rather one that simply dictates when a spike in an individual neuron should release a bit of transmitter. When that is in hand, (and when these chips even have any transmitter function built into them), then perhaps getting practical results out of 576-neuron network will be more straightforward.

source : http://www.extremetech.com/extreme/193532-darpas-new-autonomous-quadcopter-is-powered-by-a-brain-like-neuromorphic-chip

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