Mcl Vaidehi Ttf 2
Mcl Vaidehi Ttf 2
Mcl Vaidehi Ttf 2
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C++: Multiply the pointer to a vector
I was wondering if there is any more efficient way of doing something like this:
vector a, b;
vector* c = new vector;
*c = b * a;
delete c;
I have to create a function that returns a pointer to a temporary vector which then multiplies itself with another vector and then delete it after. Is this going to have a huge impact on the performance?
A:
You might be interested in vector::operator*= (see the Complex Elements section for rules of the form new()->something).
A:
Using the operator*= should be fine. See the overloads for vector in the standard that you can use to multiply vectors that contain a float type. It is made pretty clear that only the result of a vector *= itself should be destroyed.
Though you might want to rethink the design. Creating your own vector as a temporary before calling the operator*= will be less expensive and you will not have to worry about the lifetime of the temporary.
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mcl vaidehi ttf 2Measuring and treatment effects in international children’s welfare experiments.
In this article, the authors apply methods of statistics that have been used traditionally to test differences in means but apply them to the evaluation of treatment effects. The results of a laboratory-based experiment focusing on children who are maltreated are described to demonstrate the application of these methods. Application of these methods to group-randomized studies such as the one described is possible because intervention and control groups are similar. In the group-randomized study, differences between the groups must be allowed for. These differences are estimated and described to the researcher. If the researcher wants to use the children as experimental units, the following alternative results can be generated: 1) The means in each group are not different; the variance in each group is different, and the numbers of children in each group (or subject of the study) are different. 2) The means are different, the variance is the same, and the numbers of children in each group are the same. 3) The means are different and the variances differ; the numbers of children in each group are different. An understanding of the sampling error generated by the group-randomized approach and its application to the descriptive data, as well as the ability to employ the results of the statistical procedure, will increase the likelihood of valid conclusions from the analysis.Q:
find unique columns that are in common between two dataframes in Python
I have two pandas dataframes: df_1 and df_2.
How would I get the unique columns that are common between df_1 and df_2?
The variables in the columns are IDs in these data frames so the answer should be a dictionary of the variables in common, the ID that they belong to, and an index from df_1 or df_2 that holds the row where the variable was found. So it should look something like this:
{“ID1”, “ID2”, “A”, “B”, “A”, “C”}
A:
This is probably most straight-forward using the Pandas map() function.
>>> df_1.columns = df_2.columns
>>> data = df_1.map(df_2)
>>> data
ID2 A B C ID1
0 A