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you are not blithe, but I think I might actually prefer that
Sorry, but the analogy was legitimate. The logic behind the statement I quoted was flawed and the analogy was apt. Maybe I should have done something about Darrin choosing a lie but WC is just too easy.
As an aside, letting off WC may not be in our best interest. I personally have found his attempts at trolling 'liberals' much more enjoyable than his typical blithe political stupidity.
you are not blithe, but I think I might actually prefer that
Who said anything about an analogy? I certainly didn't. I was simply pointing out that RG said he was cool with Obama acting on incomplete data.
fuzzymath.
Here are the public sector jobs municipal and state govt protected with stimulus funds.
As Public Sector Sheds Jobs, Blacks Are Hit Hardest
http://www.truth-out.org/print/9745
As for USPS, Congress has tied USPS' hands badly, like forcing it to pay forward pension funds, etc, that is driving it to bankruptcy.
Reading comprehension is poor as well. Try reading the post RG posted that you responded to and think about how what you said relates. If you still have trouble figuring it out I will go point by point.
Where is the logical inconsistency or are you just using the old routine of blanket statement to confirm bias rather then thinking things through?
Sorry Fuzzy, I'll be sure to quote everything in the thread for you since you only take things one post at a time.
I was also unaware you would get so hurt over all of this.
I am hostile to dishonesty and stupidity. Deal with it.
I take you quoting something means that your post is in response to that quoted post. Thats what I do when I quote a post. Now you are claiming that is not what you do.
We can argue about the meanings of the word incomplete versus incorrect now if you would like because that is the crux of your disconnect.
http://library.thinkquest.org/29483/fuzzy_math.shtml
http://en.m.wikipedia.org/wiki/Fuzzy_mathematicsFuzzy math differs from conventional math primarily in the area of set theory. For example in a conventional AND statement both statements must be true for the statement to be true. However, in fuzzy logic statements are not always true or false, they merely have varying levels of confidence. The table below exibits the differences in how an AND statement is applied in conventional and fuzzy systems. As can clearly be seen the AND statement in a fuzzy system is merely the minimum confidence value of the two values.
Fuzzy mathematics forms a branch of mathematics related to fuzzy set theory and fuzzy logic. It started in 1965 after the publication of Lotfi Asker Zadeh's seminal work Fuzzy sets. [1] A fuzzy subset A of a set X is a function A:X→L, where L is the interval [0,1]. This function is also called a membership function. A membership function is a generalization of a characteristic function or an indicatorfunction of a subset defined for L = {0,1}. More generally, one can use a complete lattice L in a definition of a fuzzy subset A . [2]The evolution of the fuzzification of mathematical concepts can be broken down into three stages: [3]
1. straightforward fuzzification during the sixties andseventies, 2. the explosion of the possible choices in the generalization process during the eighties, 3. the standardization, axiomatizationandL-fuzzification inthe nineties.
Usually, a fuzzification of mathematical concepts is based on a generalization of these concepts from characteristic functions to membership functions. Let A and B be two fuzzy subsets of X. Intersection A ∩ B and union A ∪ B are defined as follows: (A ∩ B)(x) = min(A(x),B(x)), (A ∪ B)(x) = max(A(x),B(x)) for all x ∈ X.
Instead of min and max one can use t-norm and t-conorm, respectively , [4] for example, min(a,b) can be replaced by multiplication ab. A straightforward fuzzification is usually based on min and max operations because in this case more properties of traditional mathematics can be extended to the fuzzy case.
A very important generalization principle used in fuzzification of algebraic operations is a closure property. Let * be a binary operation on X. The closure property for a fuzzy subset A of X is that for all x,y ∈ X, A(x*y) ≥ min(A(x),B(x)). Let (G,*) be a group and A a fuzzy subset of G. Then A is a fuzzy subgroup of G if for all x,y
in G, A(x*y −1 ) ≥ min(A(x),A(y −1 )).
A similar generalization principle is used, for example, for fuzzification of the transitivity property. Let R be a fuzzy relation in X, i.e. R is a fuzzy subset of X×X. Then R is transitive ifforall x,y,z in X, R(x,z) ≥ min(R(x,y),R(y,z)).
Now back to your regularly scheduled programming.....
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