![]() ![]() ![]() x is a NumPy array holding the optimal values of the decision variables. I don't think there is a much easier and more intuitive way to express these outer products than with matrix multiplications. Linear programming is a set of techniques used in mathematical programming. The constraints are a little bit more complicated due to the outer products. This part can be written with a summation. The matrix form of the objective is similar to the one we saw in the section on the transportation problem. The Disciplined geometric programming section shows how to solve log-log convex programs. The Basic examples section shows how to solve some common optimization problems in CVXPY. In this section I'll discuss some modeling issues when implementing a simple transportation model in CVXPY, and compare this to a standard GAMS implementation.Īs an example consider the standard transportation model. These examples show many different ways to use CVXPY. It discusses some of underlying ideas of CVXPY and Disciplined Convex Programming. This model is very difficult to deal with in CVXPY.Īll these models are candidates for CVXPY: the models are linear or convex and they only use vectors and matrices. ![]() CVXPY does not support sparse variables (only sparse data). This is not very easily expressed in CVXPY. The matrix notation becomes a bit more cumbersome. A linearized non-convex quadratic model with binary variables.Very simple but nevertheless interesting to look at. When overdoing things, the notation becomes less intuitive.īelow I will try to formulate four models in CVXPY and see how the matrix notation will work for these examples: Although matrix notation can be very powerful, there are limits. and a very compact matrix based modeling paradigm. In addition CVXPY provides many high level functions (e.g. In that case the solver will accept it and try to find local solutions. This is a bit different from say passing on a non-convex model to a local NLP solver. It rigorously checks the model is convex which is very convenient: many convex solvers are thoroughly confused when passing on a non-convex model. However, I want to know why it is not working when I use the same strategy as the tutorial and how to get it to work in a similar fashion.Īny help would be much appreciated.CVXPY is a popular Python based modeling tool for convex models. When I replace the line clf.fit(train, labels) with clf.fit(vectors_train, labels), the error goes away. labels is an array, and the error specifically points to the first parameter being the problem, so is there a data type conversion I have to do? 9 Answers Sorted by: 359 Possible reason 1: trying to create a jagged array You may be creating an array from a list that isn't shaped like a multi-dimensional array: numpy.array ( 1, 2, 2, 3, 4) wrong numpy.array ( 1, 2, 2, 3, 4) wrong In these examples, the argument to numpy.array contains sequences of different lengths. What array element is being set to a sequence, and where is this sequence? I'm also aware that train is a DataFrame object, and that the fit() function takes in two parameters, both of which must be array-like. I am confused by what the error actually means. Labels_train, uniques = pd.factorize(train, sort = True)Ĭlf.fit(train, labels) # Value error occurs here Train, test = dataframe=True], dataframe=False] TestingData =, 0.77],, 30],, 0.77],, 0.77]]ĭataframe_training = pd.DataFrame(trainingData)ĭataframe_testing = pd.DataFrame(testingData)įrames = ĭataframe.rename(index = str, columns = ) ValueError: setting an array element with a sequence.įrom sklearn.ensemble import RandomForestClassifierįrom trics import confusion_matrix When I call the fit() function, I get the following error: In my code, I have 1 feature (1 column in the data table), and each entry in a column is a numpy array. In the tutorial, there are 4 features (4 columns in the data table), and each entry in a column is a number. My code follows this tutorial line by line, but the only major difference is the structure of the data. I have been using this tutorial as a guide. As part of a project, I am trying to use the random forest classifier from Python's SKLearn library. ![]()
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