A computer peripheral, or peripheral device, is an external object that provides input and output for the computer. Some common input devices include:
keyboard
mouse
touch screen
pen tablet
joystick
MIDI keyboard
scanner
digital camera
microphone
<span>
Some common Output Devices :
</span>monitor
projector
TV screen
printer
plotter
<span>speakers</span>
Answer:
Static scoping: x is 5
Dynamic scoping : x is 10.
Explanation:
Static scoping :
In static scoping the variable of a function take the value within the function.
If there is no values exist within the function then take the global value of the variable.
var x // No value is assigned to x so it check global value of x
function sub1() {
document.write(“x = “ + x + “”); // So it print x = 5
}
function sub2() {
var x;
x = 10;
sub1();
}
x = 5; // It is the global value of x
sub2();
Static scoping: x is 5
Dynamic scoping :
In Dynamic scoping the variable of a function take the value all the calling function ends.
If the global value is the last assigned value of a variable then it take that value.
If there exist some other function after global variable value if that function contain the variable with some assigned value variable take that value.
var x
function sub1() {
document.write(“x = “ + x + “”);
}
x = 5; // At this point x value is 5 and check there exist a function
sub2(); // So now call this function
function sub2() {
var x;
x = 10; // The value of x = 5 is replaced with x = 10
sub1();
}
Dynamic scoping : x is 10.
Bayes’ Theorem provides a way that we can calculate the probability of a piece of data belonging to a given class, given our prior knowledge.
P(class|data) = (P(data|class) * P(class)) / P(data)
Where P(class|data) is the probability of class given the provided data.
Explanation:
- Naive Bayes is a classification algorithm for binary and multiclass classification problems.
- It is called Naive Bayes or idiot Bayes because the calculations of the probabilities for each class are simplified to make their calculations tractable.
This Naive Bayes tutorial is broken down into 5 parts:
Step 1: Separate By Class : Calculate the probability of data by the class they belong to, the so-called base rate. Separate our training data by class.
Step 2: Summarize Dataset : The two statistics we require from a given dataset are the mean and the standard deviation
The mean is the average value and can be calculated using :
mean = sum(x)/n * count(x)
Step 3: Summarize Data By Class : Statistics from our training dataset organized by class.
Step 4: Gaussian Probability Density Function : Probability or likelihood of observing a given real-value. One way we can do this is to assume that the values are drawn from a distribution, such as a bell curve or Gaussian distribution.
Step 5: Class Probabilities : The statistics calculated from our training data to calculate probabilities for new data. Probabilities are calculated separately for each class. This means that we first calculate the probability that a new piece of data belongs to the first class, then calculate the second class, on for all the classes.
Explanation:
tools, machinery, and other durable equipment.
A: Making games too Customizable
