Collection of machine learning algorithms implemented in Python as part of a project funded by the Swiss Innovation Agency (36779.1 IP-ENG), read more about it here: https://www.aramis.admin.ch/Grunddaten/?ProjectID=44523.
The algorithms have been implemented as standalone classes; this leads to redundancy and more code because the same functions are present in several files, but it easier to modify or implement them as part of A.I. solutions.
The regression and classification algorithms are based on stochastic gradient descent (and the clustering algorithm is based on expectation-maximization). Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function, e.g. mean squared error in regression or binary cross-entropy in classification. Gradient descent is based on the observation that if the multi-variable function C(W) is defined and differentiable in a neighborhood of a point W, then C(W) decreases fastest if one goes from W in the direction of the negative gradient of C at W, -dC/dW.
However, by including only a randomly selected subset of data in the computation of the gradient, the algorithm becomes stochastic since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data).
from StandardScaler import StandardScaler
from NeuralNetworkRegressor import NeuralNetworkRegressor
import numpy as np
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
As a guidline, it is recommended to scale data before training, e.g. using a standard scaling (zero mean, unit variance).
scaler = StandardScaler()
X, y = load_boston(return_X_y=True)
X, y = scaler.fit_transform(X), scaler.fit_transform(y.reshape(-1, 1))
nnr = NeuralNetworkRegressor(hidden_layer_sizes=(10,), alpha=1.0)
nnr.fit(X, y)
y_pred = nnr.predict(X)
from StandardScaler import StandardScaler
from LogisticClassifier import LogisticClassifier
from sklearn.metrics import precision_recall_curve
import numpy as np
from sklearn.datasets import load_breast_cancer
import matplotlib.pyplot as plt
As a guidline, it is recommended to scale data before training, e.g. using a standard scaling (zero mean, unit variance).
scaler = StandardScaler()
X, y = load_breast_cancer(return_X_y=True)
X = scaler.fit_transform(X)
lc = LogisticClassifier(alpha=1.0)
lc.fit(X, y)
y_pred = lc.predict(X)
