Hello Jason, Thank you! It seems that you are quite busy now greeting to the newbies. :-) I am waiting for someone to review my proposal. Could you please tell me to whom I should reach out?
Regards, Zhiqi 在 2019年3月28日星期四 UTC+1下午5:14:46，Jason Moore写道： > > Zhiqi, > > This looks well thought out a first glance. Check out > https://github.com/sympy/sympy/wiki/GSoC-2019-Student-Instructions if you > haven't yet. > > Jason > moorepants.info > +01 530-601-9791 > > > On Wed, Mar 27, 2019 at 12:13 AM <zhiq...@gmail.com <javascript:>> wrote: > >> Hello, >> >> >> My name is Zhiqi KANG, I am a 4th year undergraduate of a 5-year >> engineering institution: Université de Technologie de Compiègne, France. I >> am interested in the project Linear algebra: Tensor core.Here is the link >> for the description of project idea. >> https://github.com/sympy/sympy/wiki/GSoC-2019-Ideas#linear-algebra-tensor-core >> Even >> though I am not very familiar with the tensor in the physical field, its >> principal in the mathematical field is quite interesting. I have >> precisely looked at the requirement of this project and make sure that I am >> capable to accomplish most of its task. However, there are still many >> questions that I would like to discuss with all contributors of SymPy and >> expecially with the mentor. One urgent problem is that I don't find the >> name of mentor for this project, so I don't really know who I should CC. >> Could you please help me to find the mentor for this project? >> >> >> Please review this draft proposal and tell me what to be ameliorated. >> Thank you! >> >> >> >> Ø Better Algorithms for sparse array: >> >> The idea is to manipulate directly les arrays in the sparse array level. >> Casting sparse arrays to a dense array and then operating is kind of a >> redundancy. I have found an example in tomatrix() function of >> sympy\sympy\tensor\array\sparse_ndim_array.py where we convert the >> sparse_array to a new dictionay and then cast it to matrix.(Code bellow) >> >> But I cannot find more cases in the array/tensor module, it would be >> great if some one can help me find out where other cases are. >> >> >> from sympy.matrices import SparseMatrix >> >> if self.rank() != 2: >> >> raise ValueError('Dimensions must be of size of 2') >> >> mat_sparse = {} >> >> for key, value in self._sparse_array.items(): >> >> mat_sparse[self._get_tuple_index(key)] = value >> >> return SparseMatrix(self.shape[0], self.shape[1], mat_sparse) >> >> >> >> Ø NumPy-like operations >> >> We have now some operations for arrays in SymPy: >> >> ² arrayfy >> >> ² tensor product >> >> ² derivatives by array >> >> ² permute dimension >> >> ² contraction >> >> For this part of project, I am planning to implement some operations such >> as: >> >> ² sum >> >> ² divide/multiply(element wise) >> >> ² any >> >> ² comparators(greater/less/equal) >> >> ² logical operator(and/or/not/xor) >> >> ² random >> >> >> >> Ø lazy operators on arrays >> >> lazy evaluation can improve the performance while iterating the array >> since it creates value only if it is called. To implement lazy operators, I >> am thinking about two plans: >> >> 1. Create a new sub-module named lazy-array (larray) of which most >> of the operations are lazy evaluated. A standard Array can be cast to a >> lazy-array by simply calling the constructor of larray and passing it as >> parameter. By doing so, users can choose whatever they want in the module >> level, which means that to manipulate a simple array or a lazy array. >> >> >> >> 1. Create a lazy version for les operators mentioned above. The >> lazy operators are accessible for a specific purpose. This implementation >> focusses on a function level for calling lazy evaluated operations, which >> means to call a simple inverse_matrix function or a lazy one. >> >> >> >> Besides, I have found in sympy\sympy\tensor\tensor.py a class >> _TensorDataLazyEvaluator which can be an example for me to implement these >> functionalities. It has methods like delete item, inverse matrix, etc. >> >> >> >> Ø code generation for arrays and array operators >> >> This part of project should be involved with another GSoC project purely >> for code generation. I would like to discuss with the mentor of the codegen >> project to have a better point of view for it. >> >> I have had an internship for 6 months in BNP Paribas Securities Services >> in Paris as developer. During this period, I have similarly worked on code >> generation task, except that the programming language is C#.( I was using >> EntityFramework and T4 by Microsoft) I believe that this experience can >> help me to get familiar with the code generation process in this project. >> >> >> >> Ø Integration over indexed symbols and arrays >> >> Firstly, I would like to talk about integration over arrays: >> >> Can we imagine the array as a set of coordinates? Suppose that we have a >> array A, say 2-dimension as (p, q). We can image two axis x and y so that >> index i and j are coordinates for the point Pij in axis x and axis y. The >> value A[pi,qj] should be the coordinate of axis z. By assuming this, we can >> use a Riemann integral or Lebesgue integral to calculate its integration >> like summing the column in the 3D space. >> >> I don’t know if this idea is correct, I would love to discuss it with you! >> >> >> >> Secondly, for integration over indexed symbols, I don’t really know what >> it means. Should the output be an expression rather than a value? It would >> be great if someone can show me with an example, thanks! >> >> >> >> Ø Equation solving with indexed symbols. >> >> I am not very familiar with this topic either. Should the result be an >> expression as well? It would be great if someone can show me with an >> example, thanks! >> >> >> Ø Implement some well-known tensor math >> >> If the time permits, I would be glad to do the extra part of this >> project. But I don’t know very well relativity, electromagnetism, etc. >> It would take me some time to better understand the principles and start to >> work on it. However, I do find some math formula that associated with this >> topic. https://en.wikipedia.org/wiki/Integral >> >> >> Ø Unify the various SymPy module >> >> To be done. >> >> >> -- >> You received this message because you are subscribed to the Google Groups >> "sympy" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to sy...@googlegroups.com <javascript:>. >> To post to this group, send email to sy...@googlegroups.com <javascript:> >> . >> Visit this group at https://groups.google.com/group/sympy. >> To view this discussion on the web visit >> https://groups.google.com/d/msgid/sympy/a5ff49d3-63e4-45e5-b87f-4b9d6be30085%40googlegroups.com >> >> <https://groups.google.com/d/msgid/sympy/a5ff49d3-63e4-45e5-b87f-4b9d6be30085%40googlegroups.com?utm_medium=email&utm_source=footer> >> . >> For more options, visit https://groups.google.com/d/optout. >> > -- You received this message because you are subscribed to the Google Groups "sympy" group. 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