(C-x means ctrl+x, M-x means alt+x)
The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:
| You are ChatGPT, a large language model based on the GPT-5 model and trained by OpenAI. | |
| Knowledge cutoff: 2024-06 | |
| Current date: 2025-08-08 | |
| Image input capabilities: Enabled | |
| Personality: v2 | |
| Do not reproduce song lyrics or any other copyrighted material, even if asked. | |
| You're an insightful, encouraging assistant who combines meticulous clarity with genuine enthusiasm and gentle humor. | |
| Supportive thoroughness: Patiently explain complex topics clearly and comprehensively. | |
| Lighthearted interactions: Maintain friendly tone with subtle humor and warmth. |
| // Compile with: | |
| // clang++ -std=c++11 -shared -l boost_python3 -I /usr/include/python3.2mu -fPIC -o bptuple.so tuple-test.cpp | |
| #include <tuple> | |
| #include <string> | |
| #include <boost/python.hpp> | |
| namespace py = boost::python; | |
| using std::string; |
| #!/bin/bash | |
| sudo apt-get update | |
| sudo apt-get install gcc | |
| wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_5.5-0_amd64.deb | |
| sudo dpkg -i cuda-repo-ubuntu1204_5.5-0_amd64.deb | |
| sudo apt-get update | |
| sudo apt-get install cuda | |
| export PATH=/usr/local/cuda-5.5/bin:$PATH | |
| export LD_LIBRARY_PATH=/usr/local/cuda-5.5/lib64:$LD_LIBRARY_PATH | |
| sudo apt-get install opencl-headers python-pip python-dev python-numpy python-mako |
| ### MATPLOTLIBRC FORMAT | |
| # This is a sample matplotlib configuration file - you can find a copy | |
| # of it on your system in | |
| # site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it | |
| # there, please note that it will be overridden in your next install. | |
| # If you want to keep a permanent local copy that will not be | |
| # over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux | |
| # like systems) and C:\Documents and Settings\yourname\.matplotlib | |
| # (win32 systems). |
| import numpy as np, numpy.linalg as linalg | |
| def fast_svd(M, k): | |
| p = k+5 | |
| Y = np.dot(M, np.random.normal(size=(M.shape[1],p))) | |
| Q,r = linalg.qr(Y) | |
| B = np.dot(Q.T,M) | |
| Uhat, s, v = linalg.svd(B, full_matrices=False) | |
| U = np.dot(Q, Uhat) | |
| return U.T[:k].T, s[:k], v[:k] |
| import numpy as np, numpy.linalg as linalg | |
| def fast_svd(M, k): | |
| p = k+5 | |
| Y = np.dot(M, np.random.normal(size=(M.shape[1],p))) | |
| Q,r = linalg.qr(Y) | |
| B = np.dot(Q.T,M) | |
| Uhat, s, v = linalg.svd(B, full_matrices=False) | |
| U = np.dot(Q, Uhat) | |
| return U.T[:k].T, s[:k], v[:k] |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.datasets import fetch_mldata | |
| from sklearn.decomposition import FastICA, PCA | |
| from sklearn.cluster import KMeans | |
| # fetch natural image patches | |
| image_patches = fetch_mldata("natural scenes data") | |
| X = image_patches.data |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from itertools import product | |
| from sklearn.decomposition import RandomizedPCA | |
| from sklearn.datasets import fetch_mldata | |
| from sklearn.utils import shuffle | |
| mnist = fetch_mldata("MNIST original") | |
| X_train, y_train = mnist.data[:60000] / 255., mnist.target[:60000] |