Answer:
Here is the script:
function dd = functionDMS(dd)
prompt= 'Enter angle in DD form ';
dd = input(prompt)
while (~checknum(dd))
if ~checknum(dd)
error('Enter valid input ');
end
dd = input(prompt)
end
degrees = int(dd)
minutes = int(dd - degrees)
seconds = ( dd - degrees - minutes / 60 ) * 3600
print degrees
print minutes
print seconds
print dd
Explanation:
The script prompts the user to enter an angle in decimal degree (DD) form. Next it stores that input in dd. The while loop condition checks that input is in valid form. If the input is not valid then it displays the message: Enter valid input. If the input is valid then the program converts the input dd into degrees, minutes and seconds form. In order to compute degrees the whole number part of input value dd is used. In order to compute the minutes, the value of degrees is subtracted from value of dd. The other way is to multiply remaining decimal by 60 and then use whole number part of the answer as minutes. In order to compute seconds subtract dd , degrees and minutes values and divide the answer by 60 and multiply the entire result with 3600. At the end the values of degrees minutes and seconds are printed. In MATLAB there is also a function used to convert decimal degrees to degrees minutes and seconds representation. This function is degrees2dms.
Another method to convert dd into dms is:
data = "Enter value of dd"
dd = input(data)
degrees = fix(dd);
minutes = dd - degrees;
seconds = (dd-degrees-minutes/60) *3600;
Answer:
Kindly check Explanation.
Explanation:
Machine Learning refers to a concept of teaching or empowering systems with the ability to learn without explicit programming.
Supervised machine learning refers to a Machine learning concept whereby the system is provided with both features and label or target data to learn from. The target or label refers to the actual prediction which is provided alongside the learning features. This means that the output, target or label of the features used in training is provided to the system. this is where the word supervised comes in, the target or label provided during training or teaching the system ensures that the system can evaluate the correctness of what is she's being taught. The actual prediction provided ensures that the predictions made by the system can be monitored and accuracy evaluated.
Hence the main difference between supervised and unsupervised machine learning is the fact that one is provided with label or target data( supervised learning) and unsupervised learning isn't provided with target data, hence, it finds pattern in the data on it's own.
A to B mapping or input to output refers to the feature to target mapping.
Where A or input represents the feature parameters and B or output means the target or label parameter.
Answer:
I think the answer are two which are B and C
Answer: layout, section, number, more options
Explanation:
Just did it on edge 2020
