Merge branch 'master' of github.com:biolab/orange3-educational

doc/index.rst

@@ -13,6 +13,7 @@ Widgets
 
    widgets/kmeans
    widgets/univariatepolynomialregression
+   widgets/gradientdescent
    widgets/polynomialclassification
 
 Indices and tables

doc/widgets/gradientdescent.rst

@@ -0,0 +1,108 @@
+Gradient Descent
+================
+
+.. figure:: icons/gradient-descent.png
+
+Educational widget that shows gradient descent algorithm on a logistic or linear regression.
+
+Signals
+-------
+
+**Inputs**:
+
+- **Data**
+
+Input data set.
+
+**Outputs**:
+
+- **Data**
+
+Data with columns selected in widget.
+
+- **Classifier**
+
+Model produced on the current step of the algorithm.
+
+- **Coefficients**
+
+Logistic regression coefficients on the current step of the algorithm.
+
+Description
+-----------
+
+This widget shows steps of `gradient descent <https://en.wikipedia.org/wiki/Gradient_descent>`__ for a logistic and
+linear regression step by step. Gradient descent is demonstrated on two attributes that are selected by user.
+
+Gradient descent is performed on logistic regression if class in data set is discrete and linear regression if class is
+continuous.
+
+.. figure:: images/gradient-descent-stamped.png
+
+1. Select two attributes (**x** and **y**) on which gradient descent algorithm is preformed.
+   Select **target class**. It is class that is classified against all other classes.
+
+2. **Learning rate** is step size in a gradient descent
+
+   With **stochastic** checkbox you can select whether gradient descent is
+   `stochastic <https://en.wikipedia.org/wiki/Stochastic_gradient_descent>`__ or not.
+   If stochastic is checked you can set **step size** that is amount of steps of stochastic gradient descent
+   performed in one press on step button.
+
+   **Restart**: start algorithm from beginning
+
+3. **Step**: perform one step of the algorithm
+
+   **Step back**: make a step back in the algorithm
+
+4. **Run**: automatically perform several steps until algorithm converge
+
+   **Speed**: set speed of automatic stepping
+
+5. **Save Image** saves the image to the computer in a .svg or .png
+   format.
+
+   **Report** includes widget parameters and visualization in the report.
+
+Example
+-------
+
+In Orange we connected *File* widget with *Iris* data set to *Gradient Descent* widget. Iris data set has discrete class
+so *Logistic regression* will be used this time.
+We connected outputs of the widget to *Predictions* widget to see how data are classified and *Data Table* widget where
+we inspect coefficients of logistic regression.
+
+.. figure:: images/gradient-descent-flow.png
+
+We opened *Gradient Descent* widget and set *X* to *sepal width* and *Y* to *sepal length*. Target class is set to
+*Iris-virginica*. We set *learning rate* to 0.02. With click in graph we set beginning coefficients (red dot).
+
+.. figure:: images/gradient-descent1.png
+
+We performs step of the algorithm with pressing **Step** button. When we get bored with clicking we can finish stepping
+with press on **Run** button.
+
+.. figure:: images/gradient-descent2.png
+
+If we want to go back in the algorithm we can do it with pressing **Step back** button. This will also change model.
+Current model uses positions of last coefficients (red-yellow dot).
+
+.. figure:: images/gradient-descent3.png
+
+In the end we want to see predictions for input data so we can open *Predictions* widget. Predictions are listed in
+left column. We can compare this predictions to real classes.
+
+.. figure:: images/gradient-descent4.png
+
+If we want to demonstrate *linear regression* we can change data set to *Housing*. That data set has a
+continuous class variable. When using linear regression we can select only one feature what means that our function
+is linear. The another parameter that is plotted in the graph is
+`intercept <https://en.wikipedia.org/wiki/Y-intercept>`__ of a
+`linear function <https://en.wikipedia.org/wiki/Linear_function>`__.
+
+This time we selected *INDUS* as a
+`independent variable <https://en.wikipedia.org/wiki/Dependent_and_independent_variables>`__.
+In widget we can make same actions as before. In the end we can also check predictions for each point with *Predictions*
+widget. And coefficients of linear regression in a *Data Table*.
+
+.. figure:: images/gradient-descent-housing.png

doc/widgets/images/gradient-descent-tags.txt

@@ -0,0 +1,5 @@
+0	92	33
+1	100	189
+2	170	372
+3	90	500
+4	119	609