added neural, code from a nice machine learning tutorial by Jason Bro…

…wnlee

neural/PythonMachineLearningExampleNotes.txt

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+This code comes from the tutorial by Jason Brownlee:
+Your First Machine Learning Project in Python Step-By-Step
+https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
+
+
+I assempled the code in IntelliJ and built a virtualenv inside the project directory
+and installed the following modules inside the venv:
+scipy, numpy, matplotlib, pandas and sklearn.
+
+I did not need to nor did I include access to all global Python modules in the
+IntelliJ project structure options.
+
+When using IntelliJ IDEA Ultimate, you enjoy the great benefit of the SciView,
+which works with Brownlee's code, matplotlib and numpy to render wonderful
+graphical plots right in IntelliJ. I was amazed when I first saw this right in the IDE.
+
+Check out the above URL. It's a great tutorial and the code works great, especially
+in IntelliJ IDEA Ultimate.
+
+On a side note, I started the project and the venv on iCloud Drive, as I use multiple
+Macs against a common cloud-synced dev area, usually before I decide something is ready
+to go into git. So, when I first tried opening the project (and hence the venv) on 
+another Mac which is running an identical set of tools like IntelliJ, I ran into a small issue.
+
+The venv has never been activate on that Mac. Initially, IntelliJ gave me a big error about an
+invalid Python interpreter. I could not solve the problem by adjusting things in the Project
+Structure dialog, by trying to configure the -existing- venv. Still throwing invalid interpreter
+error and none of the module imports were good.
+
+SOLUTION: I quit IntelliJ and I opened up a regular terminal. I activate the virtualen in that terminal
+thereby setting the environment variables etc. THEN: I LAUNCHED INTELLIJ FROM THIS TERMINAL:
+] open /path/to/intellij/inside/the/mac/application/folder
+
+It worked.
+
+So, I don't know at this point if once the venv is deactivate this problem will return.
+I am looking for a better solution, like how can I get IntelliJ to just automatically
+activate a venv inside a project.
+
+Not that one wants to store all that python venv stuff with the project, especially in a repository,
+but it would be nice to be able to download the code and maybe also intellij project settings
+and then just open the project on perhaps one of many different workstations and have
+IntelliJ automatically activate the venv and NOT throw this invalid interpretor error, which
+is especially troubling at first since it is not apparent how to resolve it from within IntelliJ
+itself, even though it seems like a straightforword Project Structure issue.
+
+Jimmy Gizmo
+

neural/irisnet.py

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+#This code comes from the tutorial by Jason Brownlee:
+#Your First Machine Learning Project in Python Step-By-Step
+#https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
+
+# Load libraries
+import pandas
+from pandas.plotting import scatter_matrix
+import matplotlib.pyplot as plt
+from sklearn import model_selection
+from sklearn.metrics import classification_report
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import accuracy_score
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.naive_bayes import GaussianNB
+from sklearn.svm import SVC
+
+# Load dataset
+url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
+dataset = pandas.read_csv(url, names=names)
+
+# shape
+print(dataset.shape)
+
+# head
+print(dataset.head(20))
+
+# descriptions
+print(dataset.describe())
+
+# class distribution
+print(dataset.groupby('class').size())
+
+# box and whisker plots
+dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
+plt.show()
+
+# histograms
+dataset.hist()
+plt.show()
+
+# scatter plot matrix
+scatter_matrix(dataset)
+plt.show()
+
+# Split-out validation dataset
+array = dataset.values
+X = array[:,0:4]
+Y = array[:,4]
+validation_size = 0.20
+seed = 7
+X_train, X_validation, Y_train, Y_validation = \
+    model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
+
+# Test options and evaluation metric
+seed = 7
+scoring = 'accuracy'
+
+# Spot Check Algorithms
+models = []
+models.append(('LR', LogisticRegression()))
+models.append(('LDA', LinearDiscriminantAnalysis()))
+models.append(('KNN', KNeighborsClassifier()))
+models.append(('CART', DecisionTreeClassifier()))
+models.append(('NB', GaussianNB()))
+models.append(('SVM', SVC()))
+# evaluate each model in turn
+results = []
+names = []
+for name, model in models:
+    kfold = model_selection.KFold(n_splits=10, random_state=seed)
+    cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
+    results.append(cv_results)
+    names.append(name)
+    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
+    print(msg)
+
+# Compare Algorithms
+fig = plt.figure()
+fig.suptitle('Algorithm Comparison')
+ax = fig.add_subplot(111)
+plt.boxplot(results)
+ax.set_xticklabels(names)
+plt.show()
+
+# Make predictions on validation dataset
+knn = KNeighborsClassifier()
+knn.fit(X_train, Y_train)
+predictions = knn.predict(X_validation)
+print(accuracy_score(Y_validation, predictions))
+print(confusion_matrix(Y_validation, predictions))
+print(classification_report(Y_validation, predictions))
+
+
+