I am reading beginner's level ML books and it seems that everyone writes
output = np.dot(inputs, np.array(weights).T) + biases
It looks awkward to me since the equation is y = wx + b, not y = xw + b. A variable is written after its coefficient.
output = np.dot(weights, np.array(inputs).T) + np.array([biases]).T
Why not this? Is there a reason for that? or just a convention?
It is a convention picked up by the authors.
Even the way the variables(their dimensions) are initialised is another convention that leads to this.
Both ways are acceptable and are correct given you implement them correctly, that is the aim of the equation is satisfied.
Related
I have been trying to solve a problem stated in an exam of coursera. I am not seeking the solution but I need to get the steps and concepts to resolve this.
Can any one share the concept and steps to help me find the solution.
UPDATE:
I was expecting a down-vote and its not unusual, as its the most easiest thing people can do. I am seeking the direction to solve the problem as I wasn't able to get the idea to solve it after watching the videos on Coursera. I hope someone sensible out there can share a direction and step to achieve the mentioned goal.
Mean Normalization
Mean normalization, also known as 'standardization', is one of the most popular techniques of feature scaling.
Andrew Ng describes it in the 12a slide of lecture 4:
How to resolve the problem
The problem asks you to standardize the first feature in the third example: midterm = 94;
well, we have just to resolve the equation!
Just for clarity, the notation:
μ (mu) = "avg value of x in training set", in other words: the mean of the x1 column.
σ (sigma) = "range (max-min)", literaly σ = max-min (of the x1 column).
So:
μ = ( 89 + 72 + 94 +69 )/4 = 81
σ = ( 94 - 69 ) = 25
x_std = (94 - 81)/25 = 0.52
Result: 0.52
Best regards,
Marco.
The first step of solving this question is to identify what is , from the content of the lecture, it refers to the first feature of the third training case. Which is the unsquared version of the midterm score in the third row of the table.
Secondly, you need to understand the concept of normalization. The reason why we need normalization is that the value of some features among all training examples may much larger than the value of other features, which may make the cost function have pretty bad shape and this will make it harder gradient descent to find the minimum. In order to solve this, we want to make all features have nearly the same scale, and make the range of the feature to be centered at zero.
In this question, we want to scale every feature to a scale of 1, in order to do this, you need to find the max and min value of the feature among all training cases. Then squeeze the range of the feature to 0 and 1. The second step is to find the center value of the feature (average value in this case) and move the center value of the feature to 0.
I think this is pretty much all hints I can give you, you will totally be able to calculate the answer to this question by yourself from this point.
I'm having trouble understanding a lecture slide in my school's machine learning course
why does the expected value of Y = f(X)? what does it mean
my understanding is that X, Y are vectors and f(X) outputs a vector of Y where each individual value (y_i) in the Y vector corresponds to a f(x_i) where x_i is the value in X at index i; But now it's taking the expected value of Y, which is going to be a single value, so how is that equal to f(X)?
X, Y (uppercase) are vectors
x_i,y_i (lowercase with subscript) are scalars at index i in X,Y
There is a lot of confusion here. First let's start with definitions
Definitions
Expectation operator E[.]: Takes a random variable as an input and gives a scalar/vector as an output. Let's say Y is a normally distributed random variable with mean Mu and Variance Sigma^{2} (usually stated as:
Y ~ N( Mu , Sigma^{2} ), then E[Y] = Mu
Function f(.): Takes a scalar/vector (not a random variable) and gives a scalar/vector. In this context it is an affine function, that is f(X) = a*X + b where a and b are fixed constants.
What's Going On
Now you can view linear regression from two angles.
Stats View
One angle assumes that your response variable-Y- is a normally distributed random variable because:
Y ~ a*X + b + epsilon
where
epsilon ~ N( 0 , sigma^sq )
and X is some other distribution. We don't really care how X is distributed and treat it as given. In that case the conditional distribution is
Y|X ~ N( a*X + b , sigma^sq )
Notice here that a,b and also X is a number, there is no randomness associated with them.
Maths View
The other view is the math view where I assume that there is a function f(.) that governs the real life process, that if in real life I observe X, then f(X) should be the output. Of course this is not the case and the deviations are assumed to be due to various reasons such as gauge error etc. The claim is that this function is linear:
f(X) = a*X + b
Synthesis
Now how do we combine these? Well, as follows:
E[Y|X] = a*X + b = f(X)
About your question, I first would like to challenge that it should be Y|X and not Y by itself.
Second, there are tons of possible ontological discussions over what each term here represents in real life. X,Y (uppercase) could be vectors. X,Y (uppercase) could also be random variables. A sample of these random variables might be stored in vectors and both would be represented with uppercase letters (the best way is to use different fonts for each). In this case, your sample will become your data. Discussions about the general view of the model and its relevance to real life should be made at random variable level. The way to infer the parameters, how linear regression algorithms works should be made at matrix and vectors levels. There could be other discussion where you should care about both.
I hope this overly unorganized answer helps you. In general if you want to learn such stuff, be sure you know what kind of math objects and operators you are dealing with , what do they take as input and what are their relevance to real life.
I would like to prove properties of expressions involving matrices and vectors (potentially large size, but size is fixed).
For example I want to prove that the outcome of an expression is a diagonal matrix or a triangular matrix, or it is positive definite, ...
To that end I'd like encode well known properties and identities from linear algebra, such as:
||x + y|| <= ||x|| + ||y||
(A * B) * C = A * (B * C)
det(A+B) = det(A) + det(B)
Tr(zA) = z * Tr(A)
(I + AB) ^ (-1) = I - A(I + BA) ^ (-1) * B
...
I have attempted to implement this in Z3. But even for simple properties it returns unknown or times out. I've tried with array theory and quantifiers.
I'd like know if this problem can be solved with an SMT solver or is it not suited for these kind of problems? Could you give a hint by giving a small example?
You can certainly use Z3 to do this.
I have constructed a small example here, which defines the identity matrix and what it means to be a diagonal matrix, and then proves that the identity matrix is diagonal.
So, it is definitely possible to do this kind of work in Z3. Though you may find you have a better time using a tool built on top of Z3 that has more interactive proving features, such as Dafny or F*.
I'm working on homework for my machine learning course and am having trouble understanding the question on Naive Bayes. The problem I have is a variation of question number 2 on the following page:
https://www.cs.utexas.edu/~mooney/cs343/hw3-old/hw3.html
The numbers I have are slightly different, so I'll replace the numbers from my assignment with the example above. I'm currently attempting to figure out the probability that the first text is physics. To do so, I have something that looks a little like this:
P(physics|c) = P(physics) * P(carbon|physics) * p(atom|physics) * p(life|physics) * p(earth|physics) / [SOMETHING]
P(physics|c) = .35 * .005 * .1 * .001 * .005 / [SOMETHING]
I'm basing this off of an example that I've seen in my notes, but I can't seem to figure out what I'm supposed to divide by. I'll provide the example from the notes as well.
Perhaps I'm going about this in the wrong way, but I'm unsure where the P(X) term that we're dividing by is coming from. How does this relate to the probability that the text is physics? I feel that getting this issue resolved will make the remainder of the assignment simple.
The denominator P(X) is just the sum of P(X|Y)*P(Y) for all possible classes.
Now, it's important to note that in Naive Bayes, you do not have to compute this P(X). You only have to compute P(X|Y)*P(Y) for each class, and then select the class that produced the highest probability.
In your case, I assume you must have several classes. You mentioned physics, but there must be others like chemistry or math.
So you can compute:
P(physics|X) = P(X|physics) * P(physics) / P(X)
P(chemistry|X) = P(X|chemistry) * P(chemistry) / P(X)
P(math|X) = P(X|math) * P(math) / P(X)
P(X) is the sum of P(X|Y)*P(Y) for all classes:
P(X) = P(X|physics)*P(physics) + P(X|chemistry)*P(chemistry) + P(X|math)*P(math)
(By the way, the above statement is exactly analogous to the example in the image that you provided. The equations are a bit complicated there, but if you rearrange them, you will find that P(X) = P(X|positive)*P(positive) + P(X|negative)*P(negative) in that example).
To produce the answer (that is, to determine Y among physics, chemistry, or math), you would select the maximum value among P(physics|X), P(chemistry|X), and P(math|X).
As I mentioned, you do not need to compute P(X) because this term exists in the denominator of all of P(physics|X), P(chemistry|X), and P(math|X). Thus, you only need to determine the max among P(X|physics)*P(physics), P(X|chemistry)*P(chemistry), and P(X|math)*P(math).
The point is that you don't really need a value for P(x) because it is the same among all classes. So you should ignore it and just compare the numbers before the division step. The highest number is the predicted class.
The reason it is in the equation is originating from the Bayes rule:
P(C1|X) = P(X|C1) * P(C1) / P(X)
I am a relative newcomer to R and not a mathematician but a geneticist. I have many sets of multiple pairs of data points. When they are plotted they yield a flattened S curve with most of the data points ending up near the zero mark. A minority of the data points fly far off creating what is almost two J curves, one down and one up. I need to find the inflection points where the data sharply veers upward or downward. This may be an issue with my math but is seems to me that if I can smooth and fit a curve to the line and get an equation I could then take the second derivative of the curve and determine the inflection points from where the second derivative changes sign. I tried it in excel and used the curve to get approximate fit to get the starting formula but the data has a bit of "wiggling" in it so determining any one inflection point is not possible even if I wanted to do it all manually (which I don't). Each of the hundreds of data sets that I have to find these two inflection points in will yield about the same curve but will have slightly different inflection points and determining those inflections points precisely is absolutely critical to the problem. So if I can set it up properly once in an equation that should do it. For simplicity I would like to break them into the positive curve and the negative curve and do each one separately. (Maybe there is some easier formula for s curves that makes that a bad idea?)
I have tried reading the manual and it's kind of hard to understand likely because of my weak math skills. I have also been unable to find any similar examples I could study from.
This is the head of my data set:
x y
[1,] 1 0.00000000
[2,] 2 0.00062360
[3,] 3 0.00079720
[4,] 4 0.00085100
[5,] 5 0.00129020
(X is just numbering 1 to however many data points and the number of X will vary a bit by the individual set.)
This is as far as I have gotten to resolve the curve fitting part:
pos_curve1 <- nls(curve_fitting ~ (scal*x^scal),data = cbind.data.frame(curve_fitting),
+ start = list(x = 0, scal = -0.01))
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
Am I just doing the math the hard way? What am I doing wrong with the nls? Any help would be much much appreciated.
Found it. The curve is exponential not J and the following worked.
fit <- nls(pos ~ a*tmin^b,
data = d,
start = list(a = .1, b = .1),
trace = TRUE)
Thanks due to Jorge I Velez at R Help Oct 26, 2009
Also I used "An Appendix to An R Companion to Applied Regression, second edition" by John Fox & Sanford Weisberg last revision 13: Dec 2010.
Final working settings for me were:
fit <- nls(y ~ a*log(10)^(x*b),pos_curve2,list(a = .01, b = .01), trace=TRUE)
I figured out what the formula should be by using open office spread sheet and testing the various curve fit options until I was able to show exponential was the best fit. I then got the structure of the equation from that. I used the Fox & Sanford article to understand the way to set the parameters.
Maybe I am not alone in this one but I really found it hard to figure out the parameters and there were few references or questions on it that helped me.