From 7cc394250c39fc1596e76cd82d776f7c3bda645e Mon Sep 17 00:00:00 2001
From: benj3542 <s200431@student.dtu.dk>
Date: Mon, 20 Mar 2023 14:11:30 +0100
Subject: [PATCH] 3D pca code
---
.DS_Store | Bin 6148 -> 6148 bytes
FeatureEstimation.py | 1 +
PCA.py | 53 +++++++++++++++++++++++++++++++++++++++++++
3 files changed, 54 insertions(+)
create mode 100644 PCA.py
diff --git a/.DS_Store b/.DS_Store
index bc975d8d2b78991fad68a23c0ac2ab94d094180f..d6e37f5c6de2c7fc3b8f1c47f7e5b3a22889276e 100644
GIT binary patch
delta 16
XcmZoMXffEZj)~dG!eH}8reIM3FYN_1
delta 16
XcmZoMXffEZj)~dCz+&@8reIM3FZBgB
diff --git a/FeatureEstimation.py b/FeatureEstimation.py
index 4c599d4..814c531 100644
--- a/FeatureEstimation.py
+++ b/FeatureEstimation.py
@@ -35,6 +35,7 @@ the different parts easier (Default shortcut: Ctrl + Shift + O)
# Set working directory
+import numpy as np
import os
wkdir = "/home/s200431"
os.chdir(wkdir)
diff --git a/PCA.py b/PCA.py
new file mode 100644
index 0000000..48dabfb
--- /dev/null
+++ b/PCA.py
@@ -0,0 +1,53 @@
+from sklearn import decomposition
+from sklearn.preprocessing import StandardScaler
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import numpy as np
+
+
+def PCA(X, y, n_components):
+ fig = plt.figure(1, figsize=(4, 3))
+ plt.clf()
+
+ ax = fig.add_subplot(111, projection="3d", elev=48, azim=134)
+ ax.set_position([0, 0, 0.95, 1])
+
+ # Standardize the data
+ scaler = StandardScaler()
+ X = scaler.fit_transform(X)
+
+ pca = decomposition.PCA(n_components=n_components)
+ pca.fit(X)
+ X = pca.transform(X)
+
+ for name, label in [("CTRL", 0), ("PTSD", 1)]:
+ ax.text3D(
+ X[y == label, 0].mean(),
+ X[y == label, 1].mean(),
+ X[y == label, 2].mean(),
+ name,
+ horizontalalignment="center",
+ bbox=dict(alpha=0.5, edgecolor="w", facecolor="w"),
+ )
+
+ ax.scatter(
+ X[:, 0],
+ X[:, 1],
+ X[:, 2],
+ c=y,
+ cmap=plt.cm.nipy_spectral,
+ edgecolor="k",
+ )
+
+ ax.xaxis.set_ticklabels([])
+ ax.yaxis.set_ticklabels([])
+ ax.zaxis.set_ticklabels([])
+
+ plt.show()
+
+ # Save plot
+ Feature_savepath = "./Figures/"
+ plt.savefig(Feature_savepath + "PCA_3D.png", dpi=300)
+
+ return "PCA was complete without errors - check the plot in your chosen path"
--
GitLab