I am reading a paper related to resource and power allocation and I come across the formula for said title. Now the question is I could not find any formula for the finding the data rate using SINR. The formula used in the paper is below
R = ln(1 + SINR_ijk);
where SINR_x is the SINR for a user i associated with cell j on a Resource block k
Where is this formula derived from ?
This appears to be a form of Shannon's Theorem. See more at Wiki link
As an aside, this does not take into consideration coding rate, modulation and many other factors that affect data rate.
(I would have made this a comment instead of an answer, but I do not have sufficient rep yet.)
Related
It seems Matlab is giving incorrect results for multinomial logistic regression.
In their example documentation using Fisher's Iris dataset [link], they give coefficients for the model which can be used on the same data set itself to get the modeled probabilities.
load fisheriris
sp = categorical(species);
[B,dev,stats] = mnrfit(meas,sp);
PHAT=mnrval(B,meas);
However, none of the expected value aggregates match the population aggregates which is a requirement for a MaxEnt classifer (See slide 35 [here], or Eq 14 [here], or Agresti "Categorical Data Analysis" pg 298, etc.)
For example
>> sum(PHAT)
>> 49.9828 49.8715 50.1456
should all equal 50 (population values), likewise for other aggregations
If the parameters
B=[36.9450 42.6378
12.2641 2.4653
14.4401 6.6809
-30.5885 -9.4294
-39.3232 -18.2862]
were used instead then all aggregated sufficient statistics match.
Additionally it seems odd that Matlab is solving it with likelihoods, which can produce an error,
Warning: Maximum likelihood estimation did not converge. Iteration
limit exceeded. You may need to merge categories to increase observed
counts
where the only requirement, proved by MLE consideration, is that the expected values match and no likelihood evaluation is needed.
It would be a nice feature that if instead of true classes are given we can give an option for including just the aggregate information.
Submitted a technical error review within Mathworks website. Their reply:
Hello [----],
I am writing in reference to your Technical Support Case #01820504
regarding 'mnrfit'.
Thanks a lot for your patience and reporting this issue. This appears
to be unexpected behavior. It appears to be related to an existing
issue we have in our records, that "mnrfit" does not give correct
maximum likelihood estimates in certain cases. Since the "mnrfit"
function is not finding the maximum likelihood estimates for the
coefficients, we calculated the actual MLEs. When we use these
estimates, we get the desired result of all 50s in this case.
The issue is that, for this particular dataset in our example, the
classes can be separated perfectly. This means that the logistic
function, in order to get exact zero or one probabilities, needs to
have infinite coefficients. The "mnrfit" function carries out an
iterative procedure with the coefficients getting larger, but it stops
at a point where the results have the issue that you have found. We
certainly agree that "mnrfit" could be made to do better. Our
development team is working on it.
At this stage, I am not able to suggest a workaround other than to
write a custom implementation as my colleague and I had tried. For
now, I will be closing this request as I have already forwarded it to
our records. However, if you have any additional questions related to
this case, please do not hesitate to reach me.
Sincerely,
[----]
MathWorks Technical Support Department
According to authors in 1, 2, and 3, Recall is the percentage of relevant items selected out of all the relevant items in the repository, while Precision is the percentage of relevant items out of those items selected by the query.
Therefore, assuming user U gets a top-k recommended list of items, they would be something like:
Recall= (Relevant_Items_Recommended in top-k) / (Relevant_Items)
Precision= (Relevant_Items_Recommended in top-k) / (k_Items_Recommended)
Until that part everything is clear but I do not understand the difference between them and Recall rate#k. How would be the formula to compute recall rate#k?
Finally, I received an explanation from Prof. Yuri Malheiros (paper 1). Althougth recall rate#k as cited in papers cited in the questions seemed to be the normal recall metrics but applied into a top-k, they are not the same. This metric is also used in paper 2, paper 3 and paper 3
The recall rate#k is a percentage that depends on the tests made, i.e., the number of recommendations and each recommendation is a list of items, some items will be correct and some not. If we made 50 different recommendations, let us call it R (regardless of the number of items for each recommendation), to calculate the recall rate is necessary to look at each of the 50 recommendations. If, for each recommendation, at least one recommended item is correct, you can increment a value, in this case, let us call it N. In order to calculate the recall rate#R, it is neccesary to make the N/R.
Information Gain= (Information before split)-(Information after split)
Information gain can be found by above equation. But what I don't understand is what is exactly the meaning of this information gain? Does it mean that how much more information is gained or reduced by splitting according to the given attribute or something like that???
Link to the answer:
https://stackoverflow.com/a/1859910/740601
Information gain is the reduction in entropy achieved after splitting the data according to an attribute.
IG = Entropy(before split) - Entropy(after split).
See http://en.wikipedia.org/wiki/Information_gain_in_decision_trees
Entropy is a measure of the uncertainty present. By splitting the data, we are trying to reduce the entropy in it and gain information about it.
We want to maximize the information gain by choosing the attribute and split point which reduces the entropy the most.
If entropy = 0, then there is no further information which can be gained from it.
Correctly written it is
Information-gain = entropy-before-split - average entropy-after-split
the difference of entropy vs. information is the sign. Entropy is high, if you do not have much information of the data.
The intuition is that of statistical information theory. The rough idea is: how many bits per record do you need to encode the class label assignment? If you have only one class left, you need 0 bits per record. If you have a chaotic data set, you will need 1 bit for every record. And if the class is unbalanced, you could get away with less than that, using a (theoretical!) optimal compression scheme; e.g. by encoding the exceptions only. To match this intuition, you should be using the base 2 logarithm, of course.
A split is considered good, if the branches have lower entropy on average afterwards. Then you have gained information on the class label by splitting the data set. The IG value is the average number of bits of information you gained for predicting the class label.
Just starting to play around with Neural Networks for fun after playing with some basic linear regression. I am an English teacher so don't have a math background and trying to read a book on this stuff is way over my head. I thought this would be a better avenue to get some basic questions answered (even though I suspect there is no easy answer). Just looking for some general guidance put in layman's terms. I am using a trial version of an Excel Add-In called NEURO XL. I apologize if these questions are too "elementary."
My first project is related to predicting a student's Verbal score on the SAT based on a number of test scores, GPA, practice exam scores, etc. as well as some qualitative data (gender: M=1, F=0; took SAT prep class: Y=1, N=0; plays varsity sports: Y=1, N=0).
In total, I have 21 variables that I would like to feed into the network, with the output being the actual score (200-800).
I have 9000 records of data spanning many years/students. Here are my questions:
How many records of the 9000 should I use to train the network?
1a. Should I completely randomize the selection of this training data or be more involved and make sure I include a variety of output scores and a wide range of each of the input variables?
If I split the data into an even number, say 9x1000 (or however many) and created a network for each one, then tested the results of each of these 9 on the other 8 sets to see which had the lowest MSE across the samples, would this be a valid way to "choose" the best network if I wanted to predict the scores for my incoming students (not included in this data at all)?
Since the scores on the tests that I am using as inputs vary in scale (some are on 1-100, and others 1-20 for example), should I normalize all of the inputs to their respective z-scores? When is this recommended vs not recommended?
I am predicting the actual score, but in reality, I'm NOT that concerned about the exact score but more of a range. Would my network be more accurate if I grouped the output scores into buckets and then tried to predict this number instead of the actual score?
E.g.
750-800 = 10
700-740 = 9
etc.
Is there any benefit to doing this or should I just go ahead and try to predict the exact score?
What if ALL I cared about was whether or not the score was above or below 600. Would I then just make the output 0(below 600) or 1(above 600)?
5a. I read somewhere that it's not good to use 0 and 1, but instead 0.1 and 0.9 - why is that?
5b. What about -1(below 600), 0(exactly 600), 1(above 600), would this work?
5c. Would the network always output -1, 0, 1 - or would it output fractions that I would then have to roundup or rounddown to finalize the prediction?
Once I have found the "best" network from Question #3, would I then play around with the different parameters (number of epochs, number of neurons in hidden layer, momentum, learning rate, etc.) to optimize this further?
6a. What about the Activation Function? Will Log-sigmoid do the trick or should I try the other options my software has as well (threshold, hyperbolic tangent, zero-based log-sigmoid).
6b. What is the difference between log-sigmoid and zero-based log-sigmoid?
Thanks!
First a little bit of meta content about the question itself (and not about the answers to your questions).
I have to laugh a little that you say 'I apologize if these questions are too "elementary."' and then proceed to ask the single most thorough and well thought out question I've seen as someone's first post on SO.
I wouldn't be too worried that you'll have people looking down their noses at you for asking this stuff.
This is a pretty big question in terms of the depth and range of knowledge required, especially the statistical knowledge needed and familiarity with Neural Networks.
You may want to try breaking this up into several questions distributed across the different StackExchange sites.
Off the top of my head, some of it definitely belongs on the statistics StackExchange, Cross Validated: https://stats.stackexchange.com/
You might also want to try out https://datascience.stackexchange.com/ , a beta site specifically targeting machine learning and related areas.
That said, there is some of this that I think I can help to answer.
Anything I haven't answered is something I don't feel qualified to help you with.
Question 1
How many records of the 9000 should I use to train the network? 1a. Should I completely randomize the selection of this training data or be more involved and make sure I include a variety of output scores and a wide range of each of the input variables?
Randomizing the selection of training data is probably not a good idea.
Keep in mind that truly random data includes clusters.
A random selection of students could happen to consist solely of those who scored above a 30 on the ACT exams, which could potentially result in a bias in your result.
Likewise, if you only select students whose SAT scores were below 700, the classifier you build won't have any capacity to distinguish between a student expected to score 720 and a student expected to score 780 -- they'll look the same to the classifier because it was trained without the relevant information.
You want to ensure a representative sample of your different inputs and your different outputs.
Because you're dealing with input variables that may be correlated, you shouldn't try to do anything too complex in selecting this data, or you could mistakenly introduce another bias in your inputs.
Namely, you don't want to select a training data set that consists largely of outliers.
I would recommend trying to ensure that your inputs cover all possible values for all of the variables you are observing, and all possible results for the output (the SAT scores), without constraining how these requirements are satisfied.
I'm sure there are algorithms out there designed to do exactly this, but I don't know them myself -- possibly a good question in and of itself for Cross Validated.
Question 3
Since the scores on the tests that I am using as inputs vary in scale (some are on 1-100, and others 1-20 for example), should I normalize all of the inputs to their respective z-scores? When is this recommended vs not recommended?
My understanding is that this is not recommended as the input to a Nerual Network, but I may be wrong.
The convergence of the network should handle this for you.
Every node in the network will assign a weight to its inputs, multiply them by their weights, and sum those products as a core part of its computation.
That means that every node in the network is searching for some coefficients for each of their inputs.
To do this, all inputs will be converted to numeric values -- so conditions like gender will be translated into "0=MALE,1=FEMALE" or something similar.
For example, a node's metric might look like this at a given point in time:
2*ACT_SCORE + 0*GENDER + (-5)*VARISTY_SPORTS ...
The coefficients for each values are exactly what the network is searching for as it converges.
If you change the scale of a value, like ACT_SCORE, you just change the scale of the coefficient that will be found by the reciporical of that scaling factor.
The result should still be the same.
There are other concerns in terms of accuracy (computers have limited capacity to represent small fractions) and speed that may enter this, but not being familiar with NEURO XL, I can't say whether or not they apply for this technology.
Question 4
I am predicting the actual score, but in reality, I'm NOT that concerned about the exact score but more of a range. Would my network be more accurate if I grouped the output scores into buckets and then tried to predict this number instead of the actual score?
This will reduce accuracy, although you should converge to a solution much faster with fewer possible outputs (scores).
Neural Networks actually describe very high-dimensional functions in their input variables.
If you reduce the granularity of that function's output space, you essentially state that you don't care about local minima and maxima in that function, especially around the borders between your output scores.
As a result, you are sacrificing information that may be an essential component of the "true" function that you are searching for.
I hope this has been helpful, but you really should break this question down into its many components and ask them separately on different sites -- potentially some of them do belong here on StackOverflow as well.
Why is Shannon's Entropy measure used in Decision Tree branching?
Entropy(S) = - p(+)log( p(+) ) - p(-)log( p(-) )
I know it is a measure of the no. of bits needed to encode information; the more uniform the distribution, the more the entropy. But I don't see why it is so frequently applied in creating decision trees (choosing a branch point).
Because you want to ask the question that will give you the most information. The goal is to minimize the number of decisions/questions/branches in the tree, so you start with the question that will give you the most information and then use the following questions to fill in the details.
For the sake of decision trees, forget about the number of bits and just focus on the formula itself. Consider a binary (+/-) classification task where you have an equal number of + and - examples in your training data. Initially, the entropy will be 1 since p(+) = p(-) = 0.5. You want to split the data on an attribute that most decreases the entropy (i.e., makes the distribution of classes least random). If you choose an attribute, A1, that is completely unrelated to the classes, then the entropy will still be 1 after splitting the data by the values of A1, so there is no reduction in entropy. Now suppose another attribute, A2, perfectly separates the classes (e.g., the class is always + for A2="yes" and always - for A2="no". In this case, the entropy is zero, which is the ideal case.
In practical cases, attributes don't typically perfectly categorize the data (the entropy is greater than zero). So you choose the attribute that "best" categorizes the data (provides the greatest reduction in entropy). Once the data are separated in this manner, another attribute is selected for each of the branches from the first split in a similar manner to further reduce the entropy along that branch. This process is continued to construct the tree.
You seem to have an understanding of the math behind the method, but here is a simple example that might give you some intuition behind why this method is used: Imagine you are in a classroom that is occupied by 100 students. Each student is sitting at a desk, and the desks are organized such there are 10 rows and 10 columns. 1 out of the 100 students has a prize that you can have, but you must guess which student it is to get the prize. The catch is that everytime you guess, the prize is decremented in value. You could start by asking each student individually whether or not they have the prize. However, initially, you only have a 1/100 chance of guessing correctly, and it is likely that by the time you find the prize it will be worthless (think of every guess as a branch in your decision tree). Instead, you could ask broad questions that dramatically reduce the search space with each question. For example "Is the student somewhere in rows 1 though 5?" Whether the answer is "Yes" or "No" you have reduced the number of potential branches in your tree by half.