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has(jack,apples). % jack has apples.
has(jack,plums).  % jack has plums.
has(ann,plums).   % ann has plums.
has(dan,money).   % dan has money.
fruit(apples).    % apple is fruit.
fruit(plums).     % plums is fruit.

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/*
* usage: swipl -q -f prolog_hello.pl -t main
*/
main :-
    write("Hello world!"), nl, fail.

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/*
* This is a prolog code showing basic code patterns
* on how to write relationships between items and
* how to reason about them.
*/


has(jack,apples). % jack has apples.
has(jack,plums).  % jack has plums.
has(ann,plums).   % ann has plums.

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print('##############################################')
print('normal X_test predictions are below:')
print(normal_predictions[:5])
print('......................................')
print('PCA predictions are below:')
print(predictions[:5])
print('here are the real values')
print(y_test[:5])

print('################################################')

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from sklearn.decomposition import PCA
pca = PCA(n_components=2)

training_features = pca.fit_transform(X_train_scaled)
print('explained variance in train')
print(pca.explained_variance_)

clf.fit(training_features, y_train)
print('classified feature importances')
print(clf.feature_importances_)

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from sklearn.preprocessing import StandardScaler

X_train_scaled = StandardScaler().fit_transform(X_train)
X_test_scaled = StandardScaler().fit_transform(X_test)

mu = X_train_scaled.mean()

print(X_train_scaled.shape)
print('mean is:')
print(mu)

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clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(X_train, y_train)
print(clf.feature_importances_)

print('normal X_test predictions are below:')

normal_predictions = clf.predict(X_test)

print(normal_predictions[:5])

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from sklearn.model_selection import train_test_split

def get_train_test(X, y, test_size):
    """Give the train and test from X and y.
    
    To do this first combine the matrices, then split them in ratio of test_size then strip out the X and y components.
    
    """
    y_reshaped = np.array([y.T])
    combined_X_y = np.concatenate((X, y_reshaped.T), axis=1)

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y = [1000] * 51 + [2000] * 55 + [3000] * 53
y = np.asarray(y)

print('some samples of y')
print(y[:5])

print('first x shape and then y shape')

print('shape of X: {}'.format(X.shape))
print('shape of y: {}'.format(y.shape))

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column_names = ['Open', 'High', 'Low', 'Last', 'Close', 'Total Trade Quantity', 'Turnover (Lacs)']

X_hitech = df_hitech.loc[:, column_names].values
X_bhagyanagar = df_bhagyanar.loc[:, column_names].values
X_hudco = df_hudco.loc[:, column_names].values

X = np.concatenate([X_hitech, X_bhagyanagar, X_hudco], axis=0)

print(X[0])