diff --git a/exercises/02450Toolbox_Python/Scripts/ex2_2_2.py b/exercises/02450Toolbox_Python/Scripts/ex2_2_2.py
index 3d6104b2aae59b5bddd2d2afa9157d3d2e181485..d27f32b2c138d660f4a66c819dc0690387d0e0e8 100644
--- a/exercises/02450Toolbox_Python/Scripts/ex2_2_2.py
+++ b/exercises/02450Toolbox_Python/Scripts/ex2_2_2.py
@@ -14,6 +14,7 @@ from matplotlib.pyplot import (
xlabel,
ylabel,
yticks,
+ tight_layout
)
from scipy.io import loadmat
@@ -99,6 +100,7 @@ for d in range(D):
I = np.reshape(W[digit_ix, :] + X.mean(0), (16, 16))
imshow(I, cmap=cm.gray_r)
title("Reconstr.")
+tight_layout()
# Visualize the pricipal components
@@ -110,6 +112,7 @@ for k in range(K):
I = np.reshape(V[:, k], (16, 16))
imshow(I, cmap=cm.hot)
title("PC{0}".format(k + 1))
+tight_layout()
# output to screen
show()
diff --git a/exercises/02450Toolbox_Python/Scripts/ex3_3_1.py b/exercises/02450Toolbox_Python/Scripts/ex3_3_1.py
index f5cfc462ae073133074a692c8080b0706d084ee5..4fd6c531039d63145b701bc8d785c2241dcec549 100644
--- a/exercises/02450Toolbox_Python/Scripts/ex3_3_1.py
+++ b/exercises/02450Toolbox_Python/Scripts/ex3_3_1.py
@@ -37,6 +37,9 @@ sim_to_index = sorted(zip(sim,noti))
plt.figure(figsize=(12,8))
plt.subplot(3,1,1)
+# set font size
+plt.rcParams.update({'font.size': 16})
+
img_hw = int(np.sqrt(len(X[0])))
img = np.reshape(X[i], (img_hw,img_hw))
if transpose: img = img.T
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_1_1.py b/exercises/02450Toolbox_Python/Scripts/ex4_1_1.py
index e3a77ebc106a018ad928c30c1da83736957aa782..8ecfea5c64bb0a511a4bf4078c983aeaeed000ad 100644
--- a/exercises/02450Toolbox_Python/Scripts/ex4_1_1.py
+++ b/exercises/02450Toolbox_Python/Scripts/ex4_1_1.py
@@ -1,7 +1,7 @@
# exercise 4.1.1
import numpy as np
-from matplotlib.pyplot import figure, hist, plot, show, subplot, title
+from matplotlib.pyplot import figure, hist, plot, show, subplots, title
# Number of samples
N = 200
@@ -21,12 +21,10 @@ X = np.random.normal(mu, s, N).T
X = np.random.randn(N).T * s + mu
# Plot the samples and histogram
-figure(figsize=(12, 4))
-title("Normal distribution")
-subplot(1, 2, 1)
-plot(X, ".")
-subplot(1, 3, 3)
-hist(X, bins=nbins)
+fig, ax = subplots(1,2, figsize = (12,4))
+fig.suptitle("Normal distribution")
+ax[0].plot(X,".")
+ax[1].hist(X,bins=nbins)
show()
print("Ran Exercise 4.1.1")
diff --git a/exercises/02450Toolbox_Python/Scripts/ex4_3_1.py b/exercises/02450Toolbox_Python/Scripts/ex4_3_1.py
index 646d4d52b2324f2906a586b5a3da6d05997a454e..ec5bb5d786f22d1a21d77cb8916cee65d6fbea69 100644
--- a/exercises/02450Toolbox_Python/Scripts/ex4_3_1.py
+++ b/exercises/02450Toolbox_Python/Scripts/ex4_3_1.py
@@ -13,6 +13,7 @@ from matplotlib.pyplot import (
xticks,
ylim,
yticks,
+ tight_layout
)
from scipy.io import loadmat
from scipy.stats import zscore
@@ -34,6 +35,7 @@ figure()
title("Wine: Boxplot")
boxplot(X)
xticks(range(1, M + 1), attributeNames, rotation=45)
+tight_layout()
# From this it is clear that there are some outliers in the Alcohol
# attribute (10x10^14 is clearly not a proper value for alcohol content)
@@ -44,6 +46,7 @@ figure(figsize=(12, 6))
title("Wine: Boxplot (standarized)")
boxplot(zscore(X, ddof=1), attributeNames)
xticks(range(1, M + 1), attributeNames, rotation=45)
+tight_layout()
# This plot reveals that there are clearly some outliers in the Volatile
# acidity, Density, and Alcohol attributes, i.e. attribute number 2, 8,
diff --git a/exercises/02450Toolbox_Python/Scripts/ex5_2_4.py b/exercises/02450Toolbox_Python/Scripts/ex5_2_4.py
index 0790af6dbc80fc6c366a257ea8d2c4c0eddc6e9c..cb2317551793c245075d629ed6d13873e3495508 100644
--- a/exercises/02450Toolbox_Python/Scripts/ex5_2_4.py
+++ b/exercises/02450Toolbox_Python/Scripts/ex5_2_4.py
@@ -3,7 +3,7 @@ import sklearn.linear_model as lm
# requires wine data from exercise 5.1.5
from ex5_1_5 import *
-from matplotlib.pylab import figure, hist, plot, show, subplot, xlabel, ylabel
+from matplotlib.pylab import figure, hist, plot, show, subplots, xlabel, ylabel, tight_layout
# Split dataset into features and target vector
alcohol_idx = attributeNames.index("Alcohol")
@@ -21,13 +21,15 @@ y_est = model.predict(X)
residual = y_est - y
# Display scatter plot
-figure()
-subplot(2, 1, 1)
-plot(y, y_est, ".")
-xlabel("Alcohol content (true)")
-ylabel("Alcohol content (estimated)")
-subplot(2, 1, 2)
-hist(residual, 40)
+fig, ax = subplots(nrows=2,ncols=1)
+ax[0].plot(y, y_est, ".")
+ax[0].set_xlabel("Alcohol content (true)")
+ax[0].set_ylabel("Alcohol content (estimated)")
+
+ax[1].hist(residual, 40)
+ax[1].set_xlabel("Residual")
+ax[1].set_ylabel("Count")
+tight_layout()
show()
diff --git a/exercises/02450Toolbox_Python/Scripts/ex5_2_5.py b/exercises/02450Toolbox_Python/Scripts/ex5_2_5.py
index 8e44343b6b81236db68eb2642651834cbeaaca69..c7ad3fdbca3e78950bc997dff1be9b7bc6d3e681 100644
--- a/exercises/02450Toolbox_Python/Scripts/ex5_2_5.py
+++ b/exercises/02450Toolbox_Python/Scripts/ex5_2_5.py
@@ -3,7 +3,7 @@ import sklearn.linear_model as lm
# requires data from exercise 5.1.4
from ex5_1_5 import *
-from matplotlib.pylab import figure, hist, plot, show, subplot, xlabel, ylabel
+from matplotlib.pylab import figure, hist, plot, show, subplot, xlabel, ylabel, tight_layout
# Split dataset into features and target vector
alcohol_idx = attributeNames.index("Alcohol")
@@ -54,6 +54,7 @@ plot(Xfava, residual, ".r")
xlabel("Fixed*Volatile Acidity")
ylabel("Residual")
+tight_layout()
show()
print("Ran Exercise 5.2.5")
diff --git a/exercises/02450Toolbox_Python/Scripts/ex6_2_1.py b/exercises/02450Toolbox_Python/Scripts/ex6_2_1.py
index 64f7350e3bec2f9fe166dc3c74b2df93e6e7e244..e59ce50b0ff15ec7072eac9ddd077a413a3422ba 100644
--- a/exercises/02450Toolbox_Python/Scripts/ex6_2_1.py
+++ b/exercises/02450Toolbox_Python/Scripts/ex6_2_1.py
@@ -2,7 +2,7 @@
import importlib_resources
import numpy as np
import sklearn.linear_model as lm
-from matplotlib.pyplot import clim, figure, plot, show, subplot, title, xlabel, ylabel
+from matplotlib.pyplot import clim, figure, plot, show, subplot, title, xlabel, ylabel, subplots, suptitle
from scipy.io import loadmat
from sklearn import model_selection
@@ -117,13 +117,18 @@ else:
y_est= m.predict(X[:,ff])
residual=y-y_est
- figure(k+1, figsize=(12,6))
- title('Residual error vs. Attributes for features selected in cross-validation fold {0}'.format(f))
+ # Determine the number of rows and columns for subplots
+ nrows = int(len(ff) ** 0.5)
+ ncols = int(len(ff) / nrows) + (len(ff) % nrows > 0)
+
+ fig, ax = subplots(nrows=nrows, ncols=ncols, num=k+1, figsize=(12,6))
+ ax = ax.flatten()
+
+ suptitle('Residual error vs. Attributes for features selected in cross-validation fold {0}'.format(f))
for i in range(0,len(ff)):
- subplot(2, int( np.ceil(len(ff)/2)), i+1)
- plot(X[:,ff[i]],residual,'.')
- xlabel(attributeNames[ff[i]])
- ylabel('residual error')
+ ax[i].plot(X[:,ff[i]],residual,'.')
+ ax[i].set_xlabel(attributeNames[ff[i]])
+ ax[i].set_ylabel('residual error')
show()
diff --git a/exercises/02450Toolbox_Python/Scripts/ex8_2_2.py b/exercises/02450Toolbox_Python/Scripts/ex8_2_2.py
index 55b34a37765dc9e48b1e14da52f2f99406c46eda..70f5b661cd0ceeff01c1c2d93562ecaf8f158224 100644
--- a/exercises/02450Toolbox_Python/Scripts/ex8_2_2.py
+++ b/exercises/02450Toolbox_Python/Scripts/ex8_2_2.py
@@ -149,6 +149,7 @@ summaries_axes[1].set_xlabel("Fold")
summaries_axes[1].set_xticks(np.arange(1, K + 1))
summaries_axes[1].set_ylabel("Error rate")
summaries_axes[1].set_title("Test misclassification rates")
+plt.tight_layout()
# Show the plots
# plt.show(decision_boundaries.number) # try these lines if the following code fails (depends on package versions)