From 48b6e2131b130eb28fa446b0ccac00163e2b4bc9 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 8 Apr 2020 18:36:30 +0200 Subject: [PATCH] modified: ML_GFS_lib.py Added some comments, makes to code more understandable :) --- ML_GFS_lib.py | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/ML_GFS_lib.py b/ML_GFS_lib.py index ee5c135..a22529a 100644 --- a/ML_GFS_lib.py +++ b/ML_GFS_lib.py @@ -1,10 +1,11 @@ +# importing different libs that are needed import matplotlib.pyplot as plt import numpy as np import scipy as sp -import scipy.stats as stats -plt.rcParams['figure.figsize'] = [40, 20] +plt.rcParams['figure.figsize'] = [40, 20] # so the plots are bigger +# the class for the first exmple section of the GFS class FunktionAnlegen: def __init__(self, _range=10, res=.1, noise_max=2): self.range = _range @@ -17,21 +18,21 @@ class FunktionAnlegen: self.error_of_train_MSE = 0 self.error_of_test_MSE = 0 - def plot_daten(self): + def plot_daten(self): # show the training data in a scatter plot plt.scatter(*self.data) - def trainieren(self, n): - if n >= 309: + def trainieren(self, n): # train the model using the training data + if n >= 309: #numpy polyfit has a limit of 308 for the max degree, who knows why 308.... print("n is too big") raise ValueError self.fkt = np.poly1d(np.polyfit(*self.data, deg=n)) - def plot_fkt_ganzrat_fkt_n(self, n): + def plot_fkt_ganzrat_fkt_n(self, n): # plot a graph of degree n additional to the scatter plot of the training data plt.scatter(*self.data) plt.plot(np.arange(-self.range, self.range, .001), [self.fkt(x) for x in np.arange(-self.range, self.range,.001)]) plt.ylim([np.min(self.data)-10, np.max(self.data)+10]) - def plot_test_der_fkt(self, n, size): + def plot_test_der_fkt(self, n, size): # plots a scatter plot of the training and the test set and the graph of deg. n. Furthermore is the Error shown in a histogram data_points = np.random.uniform(-self.range, self.range, size) self.test_set = np.array([data_points, [self.random_data(x) for x in data_points]]) self.plot_fkt_ganzrat_fkt_n(n) @@ -40,7 +41,7 @@ class FunktionAnlegen: plt.subplot(211) plt.xlim(0,20) error_of_test = np.array([np.absolute(self.test_set[1][i]-self.fkt(x)) for i, x in enumerate(self.test_set[0])]) - self.error_of_test_MSE = np.square(error_of_test).mean() + self.error_of_test_MSE = np.square(error_of_test).mean() # MSE = Mean squared error _ = plt.hist(np.clip(error_of_test,0,self.noise_max*2), int(size/2), (0,self.noise_max*2), density=True, color='yellow', label='Fehler Testdaten: {}'.format(str(self.error_of_test_MSE))) plt.legend(fontsize=20) plt.subplot(212) @@ -50,9 +51,10 @@ class FunktionAnlegen: _ = plt.hist(np.clip(error_of_train,0,self.noise_max*2), int(size/2), (0,self.noise_max*2), density=True, label='Fehler Trainingsdaten: {}'.format(str(self.error_of_train_MSE))) plt.legend(fontsize=20) +# the class for example 2... a kNearestNeighbour Model class NearestNeighbour: def __init__(self, k, n): - self.k = k + self.k = k # the number of neigbours which will be used self.dots = np.array([np.array([np.random.rand(), np.random.rand()]) for _ in range(n)]) self.color = lambda i:'blue' if i[0]*i[1]>.25 else 'red' self.c = np.array([self.color(i) for i in self.dots]) @@ -61,12 +63,12 @@ class NearestNeighbour: self.smallest_distance = None self.c_test = None self.nearest = None - self.most_often = lambda arr: arr[np.argmax(np.unique(arr,return_counts=True)[1])] + self.most_often = lambda arr: arr[np.argmax(np.unique(arr,return_counts=True)[1])] # used to get the color, because a average isn't working with strings - def plot_daten(self): + def plot_daten(self): #plot the dots, split by color plt.scatter([i[0] for i in self.dots], [i[1] for i in self.dots], c=self.c, s=150) - def test(self): + def test(self): # plot the dots and one test sample with arrows to the nearest neighbours self.test_dot = np.array([np.random.rand(), np.random.rand()]) self.smallest_distance = [1e99 for _ in range(self.k)] self.c_test = [None for _ in range(self.k)]