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Unique (non-repeating) random numbers in O(1)?

... 22 Answers 22 Active ...
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ImportError: No module named six

... | edited Jan 5 '14 at 2:39 Uli Köhler 11.3k1212 gold badges5151 silver badges101101 bronze badges a...
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Is there a way to call a stored procedure with Dapper?

...new DynamicParameters(); p.Add("@a", 11); p.Add("@b", dbType: DbType.Int32, direction: ParameterDirection.Output); p.Add("@c", dbType: DbType.Int32, direction: ParameterDirection.ReturnValue); cnn.Execute("spMagicProc", p, commandType: CommandType.StoredProcedure); int b = p.Get<int>("...
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Print “hello world” every X seconds

... answered Oct 16 '12 at 6:05 Rohit JainRohit Jain 188k4141 gold badges353353 silver badges478478 bronze badges ...
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What's the difference between design patterns and architectural patterns?

... answered Nov 22 '10 at 7:56 pyfuncpyfunc 58.3k1414 gold badges137137 silver badges132132 bronze badges ...
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How to give Jenkins more heap space when it´s started as a service under Windows?

... answered May 9 '11 at 12:00 dunnidunni 35.3k88 gold badges9090 silver badges9292 bronze badges ...
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UTF-8, UTF-16, and UTF-32

What are the differences between UTF-8, UTF-16, and UTF-32? 12 Answers 12 ...
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“Treat all warnings as errors except…” in Visual Studio

...yGroup> ... ... <WarningsNotAsErrors>618,1030,1701,1702</WarningsNotAsErrors> </PropertyGroup> Note: 612 and 618 are both warnings about Obsolete, don't know the difference but the project i'm working on is reporting Obsolete with warning 618. ...
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Printf width specifier to maintain precision of floating-point value

...IG; double OneSeventh = 1.0/7.0; printf("%.*e\n", Digs, OneSeventh); // 1.428571428571428492127e-01 But let's dig deeper ... Mathematically, the answer is "0.142857 142857 142857 ...", but we are using finite precision floating point numbers. Let's assume IEEE 754 double-precision binary. So t...
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How to force R to use a specified factor level as reference in a regression?

... See the relevel() function. Here is an example: set.seed(123) x <- rnorm(100) DF <- data.frame(x = x, y = 4 + (1.5*x) + rnorm(100, sd = 2), b = gl(5, 20)) head(DF) str(DF) m1 <- lm(y ~ x + b, data = DF) summary(m1) Now alter the factor b...