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#Advanced symbolic system of equations solver python code#

Then drop the labels column from the dataset and then reshape the dataset to 28 by 28. Firstly, assign the labels column in our dataset to variable y_train. Since convolutional neural network works on two-dimensional data and our dataset is in the form of 785 by 1. Training Data using Convolutional Neural Network After extracting features, save the data to a CSV file. So now our dataset contains 784 features column and one label column. Now, give the corresponding label to it (For e.g., for 0–9 images same label as their digit, for – assign label 10, for + assign label 11, for times assign label 12). So there will be now 784-pixel values or features.

Now, resize the maximum area bounding rectangle to 28 by 28.

For this purpose, we calculate the area of the bounding rectangle of each contour and select the rectangle with maximum area.

Since each image in our dataset contains only one symbol/digit, we only need the bounding rectangle of maximum size.

For features, obtain the bounding rectangle of contour using ‘boundingRect’ function (Bounding rectangle is the smallest horizontal rectangle enclosing the entire contour).

To find contours use ‘findContour’ function.

Invert the image and then convert it to a binary image because contour extraction gives the best result when the object is white, and surrounding is black.

We can use contour extraction to obtain features. To remove this bias, reduce the number of images in each folder to approx. On observing the dataset, we can see that it is biased for some of the digits/symbols, as it contains 12000 images for some symbol and 3000 images for others. For simplicity, use 0–9 digits, +, ?-?and, times images in our equation solver.

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We have to use linsolve() function to solve linear equations. If there are no solutions, an EmptySet is returned The output of solveset is a FiniteSet of the solutions.

#Advanced symbolic system of equations solver python code#

The following output is obtained after executing the above code snippet − Using solveset() function, we can solve an algebraic equation as follows − The solver module in SymPy provides soveset() function whose prototype is as follows − Since x=y is possible if and only if x-y=0, above equation can be written as − The above code snippet gives an output equivalent to the below expression − SymPy provides Eq() function to set up an equation. Since the symbols = and = are defined as assignment and equality operators in Python, they cannot be used to formulate symbolic equations.