sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
65da09ef017d153aa52f27cc8c8c228145b1803d8851b4c046b6476aef02f30d | Jupyter | 27,011 | 633 | # %% [markdown]
# # Experiments with `aihwkit`
# ## Exploring the sensitivity of analog inference, analog-aware retraining and in-analog training to the non-idealities of Analog Crossbars
#
# In-memory computing uses crossbar arrays of non-volatile memories to perform Vector-Matrix Multiplications (VMMs) in-situ, i.e.... |
e17f30dfe56043605f1e144627970345773f3c4505bae6f386192db3d2f6dd34 | Jupyter | 27,214 | 917 | # %%
import argparse
import os
import numpy as np
import math
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import ... |
48f3152a54d7fd5f23745a5f2254e014178aa2c4a2afbd26c087eadd74028006 | Jupyter | 27,215 | 497 | # %%
#import packages
from skimage.io import imread, imsave
from skimage.transform import rescale
from skimage.exposure import rescale_intensity
from shapely.geometry import MultiPolygon, Polygon
from collections import defaultdict
import geopandas
import pandas as pd
import cv2
import numpy as np
import json
import ... |
2e20120cc4c7f54b01a5c8643795de410fbcf0f940fa46be1ea08b6f62de6e87 | Jupyter | 27,339 | 767 | # %% [markdown]
# # LERCause: Causal Sentence Identification with LER (Nuclear Safety Reports)
#
# This code is to run traditional machine learning models on LER data for sentence classification and prediction.
#
# Author:
# 1. Jinmo Kim: School of Information Sciences, University of Illinois Urbana-Champaign
... |
756b9783e1906c58699b160be7d6213081628ae96584d8dd4873fbee3d4d6db6 | Jupyter | 27,366 | 673 | # %% [markdown]
# # Train a GPT-2 model with Ray Train `JaxTrainer`
#
# **Time to complete**: 15 min
#
# This template shows you how to distribute a JAX/Flax training loop with [Ray Train](https://docs.ray.io/en/latest/train/train.html)'s `JaxTrainer`. You’ll train a small GPT-2-style Transformer from scratch on the ... |
0ce3d8b21cd4ba26d0888080aa3b7522f9539adb0a1527669af275450fc7fa78 | Jupyter | 27,703 | 1,162 | # %%
%load_ext autoreload
%autoreload 2
# %%
import platform
import random
import string
import uuid
import math
import os
import os.path
import skimage
import sak
import sak.signal.wavelet
import sak.data
import sak.data.augmentation
import sak.data.preprocessing
import numpy as np
import scipy as sp
import scipy.sig... |
b0abc92584fd720c73bedd72ecbe195efc1146b267fa6171b9f06b9d080d770e | Jupyter | 27,991 | 741 | # %%
"""
This script fine-tunes the Conformer project (https://github.com/eeyhsong/EEG-Conformer) and achieves better experimental results compared to the previous project.
fine-tunes by author: zhaowei701@163.com
"""
import os
gpus = [1]
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ["CUDA_VISIBLE_DEVIC... |
a92c64dcb5d224244ec45d07c2207b42085ccac4692bd1693175af098ded3382 | Jupyter | 28,046 | 978 | # %%
!pip install google-colab-selenium
# %%
import multiprocessing
import tqdm
import numpy as np
import scipy.stats as st
import scipy
import numba
import math
from bokeh.io import export_svg
from google_colab_selenium import Chrome
#import biocircuits
# Plotting modules
import bokeh.io
import bokeh.plotting
fro... |
54578596c7dd99603f0d6ee43323f06e87aa2a5a47b5feec1dfd8d6e041ae7ad | Jupyter | 28,192 | 886 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
ae4cf54e59068395b0895f8c392fa876dcc15678fbeecb9f491ad14614b9ac30 | Jupyter | 28,285 | 767 | # %%
%load_ext autoreload
%autoreload 2
# %%
import platform
import random
import uuid
import os
import os.path
import skimage
import sak
import sak.signal.wavelet
import sak.data
import sak.data.augmentation
import numpy as np
import scipy as sp
import scipy.signal
import pandas as pd
import networkx
import networkx.... |
3541202d743aac4fca3be5efdade217e7444d18e29eb550049ed26f713462dac | Jupyter | 28,434 | 822 | # %%
%load_ext autoreload
%autoreload 2
# %%
%matplotlib inline
import numpy
import random
import time
import sys
import os
import tifffile
import io
import pickle
import matplotlib
import yaml
from matplotlib import pyplot
from PIL import Image
from tqdm.auto import trange
from skimage import filters
from collecti... |
a1447b580d415ef519c11ba2df6fb7108b9c212eb043097af3b34b3957930712 | Jupyter | 28,723 | 622 | # %%
import os
import math
import time
import operator
from collections import defaultdict
import numpy as np
import pandas as pd
from scipy import stats
from sklearn.linear_model import LogisticRegression, LinearRegression, ElasticNet, Ridge
from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_... |
1dde383e2a05b54931ac01714f3d711ad0fac0e7bc87dc5b87a9a9a8d59ede7e | Jupyter | 28,835 | 939 | # %% [markdown]
# # Fit to observed
# %% [markdown]
# ## Using the only the density map output
# %% [markdown]
# ### Loading model
# %%
import torch
import torch.nn as nn
from modelsImplementation import CerealNet
# Check if CUDA (GPU) is available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu... |
121a51b4e5828268e39aea27830ad70d096f6b4664e94651e447df7751819dda | Jupyter | 28,926 | 975 | # %%
import numpy as np
import pandas as pd
import os
import torch
from torch.autograd import Variable
from matplotlib import pyplot as plt
%matplotlib inline
# %%
import sys
sys.path.append("../Network-SDE-Inference/utils")
import NeuGNN_model
import Self_func
import Interaction_func
from NeuGNN_model import *
from S... |
c179b956f40c2cfd194d6541a9d6958a6824b65ff995f9097e434d153eb75b43 | Jupyter | 28,975 | 787 | # %% [markdown]
# # Providing custom stimuli
#
# Follow this notebook to learn how to use our models for generating hypothesis about neural computations with custom stimuli.
# %% [markdown]
# **Select GPU runtime**
#
# To run the notebook on a GPU select Menu -> Runtime -> Change runtime type -> GPU.
# %%
# @markdo... |
fcc027d31454131dd96b67a10d7c0c345ef28e44fca9eec678a5556339264f0b | Jupyter | 29,011 | 778 | # %%
"""
CTNet: A Convolution-Transformer Network for EEG-Based Motor Imagery Classification
author: zhaowei701@163.com
"""
import os
gpus = [0]
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
import numpy as np
import pandas as pd
import random
import datetime
import time
fr... |
d50b4a8b7b04c6aa4691c7a1f03be0f12b2372a7db57fda07ec7430d88f39a0d | Jupyter | 29,093 | 934 | # %%
import numpy as np
import pandas as pd
import os
import torch
from torch.autograd import Variable
from matplotlib import pyplot as plt
%matplotlib inline
# %%
import sys
sys.path.append("../Network-SDE-Inference/utils")
import NeuGNN_model
import Self_func
import Interaction_func
from NeuGNN_model import *
from S... |
e439f2bba8133a9ca1b4d564b0d944eca2e41870b3904b46d14c17f8bf6b7033 | Jupyter | 29,125 | 599 | # %% [markdown]
# # Tuning XGBoost hyperparameters with Ray Tune
#
# <a id="try-anyscale-quickstart-tune-xgboost" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=tune-xgboost">
# <img src="../../_static/img/run-on-anyscale.svg" alt="try-anyscale-quick... |
58f941994b6edc6a2e61e1c74d276b27d0910be92b3506b92b6595d1db0dd573 | Jupyter | 29,689 | 662 | # %%
path_project = "/media/concha-eloko/Linux/depolymerase_building"
dir_out = f"{path_project}/seekfold_phagedepo"
import pandas as pd
import os
path_info = f"/media/concha-eloko/Linux/depolymerase_building/depolymerase_fold.csv"
info_df = pd.read_csv(path_info , sep = "\t", header = 0)
dico_folds = {"jelly-roll" :... |
51bd16ae2372a5bab09f5f39d5bbd7902e687852a3e20e319e494f3a58e29d13 | Jupyter | 29,800 | 1,029 | # %%
import numpy as np
import pandas as pd
import os
import torch
from torch.autograd import Variable
from matplotlib import pyplot as plt
%matplotlib inline
# %%
# %%
import sys
sys.path.append("../Network-SDE-Inference/utils")
import NeuGNN_model
import Self_func
import Interaction_func
from NeuGNN_model import *... |
9afca56bd53076ea176eccdf183ab286ca14f783ac1da255f5561c71a4b9733c | Jupyter | 29,967 | 677 | # %%
from endstate_correction.analysis import (
plot_overlap_for_equilibrium_free_energy,
plot_results_for_equilibrium_free_energy,
)
from endstate_correction.equ import calculate_u_kn
import pathlib
import endstate_correction
from endstate_correction.system import create_charmm_system
from openmm.app import Ch... |
cb9d03b9143893f1f5bca59aea9825a3a23a2fa6c48860bfb9668a6866555e36 | Jupyter | 30,050 | 947 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import glob
import shutil
import uuid
import random
import platform
import ecgdetectors
import torch
import torchvision
... |
44c3c42385a4d27a7eb1a3e89ed9b3212d41a9af66514c904e934bc779e1bc46 | Jupyter | 30,200 | 612 | # %% [markdown]
# # Build the depolymerase database
# ***
#
# ## The cookbook for building the depolymerase database :
# ### A. Get the E.C associated with the depolymerase activity
# ### B. Collect the IPR entries associated with the E.C of interest
# ### C. Scan the interproscan descriptions ; keep those that are ... |
440fedfc887dc3c6da9b48c8b091f7f1b0213ee5e4adc9dded2196f3a188e905 | Jupyter | 30,274 | 738 | # %% [markdown]
# # Quick Start With N-MNIST
#
# This tutorial explains all steps necessary to deploy a pretrained SNN to the devkit.
#
# 1. The pretrained SNN will be obtained by:
# - CNN-to-SNN conversion.
# - Train SNN with BPTT(Back Propagation Through Time)
#
# 2. As for the dataset, we use the ["N-MNI... |
5387d4c005afe278a0dcc5451604158864b6b8cc07a778df49227e602f022982 | Jupyter | 30,424 | 802 | # %%
"""
CTNet: A Convolution-Transformer Network for EEG-Based Motor Imagery Classification
author: zhaowei701@163.com
Due to memory constraints, the data augmentation method in LOSO classification was slightly optimized based on the approach used in subject-specific classification (main.py).
Cite this work
Zhao, ... |
b323db3464f878be5cc264aa2025d8a1ff611274fc368033bdb380aac4956be9 | Jupyter | 31,078 | 825 | # %%
# %load_ext autoreload
# %matplotlib widget
# %%
import numpy as np
from pathlib import Path
import pandas as pd
import flammkuchen as fl
import os
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap, Normalize
import seaborn as sns
from itertools import product
from skimag... |
4cdc9c7dff978681b182eba7d6624c73e69bd7eea1244a616c2a49c3d0fe1ed8 | Jupyter | 31,357 | 648 | # %% [markdown]
# ## Import all necessary packages to notebook
# %%
from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix, roc_auc_score
from sklearn.preprocessing import LabelEncoder
import json
import lightgbm as lgb
import shap
from IPython.display import clear_output
##### b... |
e34272734869ba3ef50facc12aac521392302ccb7c5bfda6aa97577d980fe095 | Jupyter | 31,655 | 651 | # %% [markdown]
# ## Import all necessary packages to notebook
# %%
from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix, roc_auc_score
from sklearn.preprocessing import LabelEncoder
import json
from xgboost import XGBClassifier
import shap
from IPython.display import clear_out... |
d66cefb093ca93dab05d3ae90e1562036492e82568356bf0c878e6c197fd39d4 | Jupyter | 32,020 | 713 | # %%
from google.colab import drive
drive.mount("/content/drive")
import os
path="/content/drive/My Drive/AMP"
os.chdir(path)
os.listdir(path)
# %%
!sudo apt install python3.8
!update-alternatives --install /usr/local/bin/python3 python3 /usr/bin/python3.8 2
!update-alternatives --install /usr/local/bin/python3 python... |
748d192b6303bb6362ea6d8252b2c195a8a0a217be8f3d998c3879be7f0448cd | Jupyter | 32,054 | 675 | # %% [markdown]
# # Machine learning and deep learning methods applied to predicting customer status
#
# Purpose: Predict active and not active clients based on the proposed data structure and naive data structure. Here, we implemented Support Vector Machines (SVM), Random Forest (RF), K-nearest Neighbours (KNN) and L... |
74d4aeebf4d0102f997ac1e7d058d8a56130ba34a855eaa445f254c1e2da96b3 | Jupyter | 32,070 | 908 | # %% [markdown]
# # ECG Latent Space Exploration
# This notebook shows how to create a latent space from a pretrained multimodal model.
# %%
import os
import sys
from typing import Callable, List, Dict
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from... |
5671b7e22f3629613fcb1a3d19520a025132e204b1980a72973549198874f292 | Jupyter | 32,250 | 829 | # %%
import omicverse as ov
from omicverse.utils import mde
import scanpy as sc
import scvelo as scv
ov.utils.ov_plot_set()
# %%
adata=scv.datasets.dentategyrus()
adata
# %%
adata.obs['celltype']=adata.obs['clusters'].copy()
# %%
import numpy as np
import pandas as pd
bulk=pd.read_csv('data/GSE74985_mergedCount.txt.... |
dbefe0efc1e7c33a2e27aac1e84f68af295794f5238cf707f214157c37469cf8 | Jupyter | 32,556 | 516 | # %% [markdown]
# <img src="img/standardized-team4.jpg" width=1400 />
# %% [markdown]
# # A standardized framework to test event-based experiments: A tutorial
# In the past decade, there has been a growing interest in improving scientific practices. Researchers have been enhancing their application of statistical test... |
60ab4bc525968f09ad6d46890e5dde2fa7088852b24dd41ebcb92c00f368ae7b | Jupyter | 32,878 | 841 | # %% [markdown]
# In this Jupyter notebook we showcase evolutionary IB-LGCA, i.e. we look at proliferating cells, where daughter cells inherit properties of mother cells plus a noise term. This idea is applied to two different models of cell proliferation and invasion and key aspects are highlighted with the help of co... |
1137bb518f691dd4e6b1089d33d64f7a25a363a661efad790c18caf38acf5635 | Jupyter | 32,883 | 885 | # %%
using Distributed
addprocs(8);
# %%
@everywhere using PulseInputDDM, MAT
using Flatten
# %%
x_joint = Vector(undef, 3);
x_choice = Vector(undef, 3);
# %%
ratnames = ["B068","T034","T036","T063","T030", "T035","T011","B053", "T080","T103","E021"];
dt, n, cross = 1e-2, 53, false
data_path = joinpath(abspath(joinp... |
2eb92c60019d4a141191199c5ed84a105b7be454135408ee8b4498d95380c081 | Jupyter | 33,123 | 924 | # %%
%load_ext autoreload
%autoreload 2
# %%
import platform
import random
import uuid
import os
import os.path
import skimage
import sak
import sak.signal.wavelet
import sak.data
import sak.data.augmentation
import numpy as np
import scipy as sp
import scipy.signal
import pandas as pd
import networkx
import networkx.... |
66ae2680793364b97b7216624040211c863d8dabf1f0925cc66b68451fe46fea | Jupyter | 33,175 | 817 | # %% [markdown]
# # Segmentation with `rising` and `PytorchLightning`
#
# This example will show you how to build a proper training pipeline with [PyTorch Lightning](https://github.com/PytorchLightning/pytorch-lightning) and [rising](https://github.com/PhoenixDL/rising).
# But first let's configure this notebook corre... |
98db6e9b87b7212b2c7488f204f2045adf6210f1378b0f4cebf55d8fe13a128e | Jupyter | 33,304 | 970 | # %%
#%matplotlib widget
import os
from os import makedirs
from pathlib import Path
import numpy as np
import pandas as pd
import copy
import matplotlib.pyplot as plt
# %%
directory = Path('../training_runs/selection_dir_comparison')
os.listdir(directory)
# %%
run_paths = {
'0.9': {
0: 'A2C_PongNoFrames... |
8980d09a30cbe4e7728188b6634ab516f1e5a9aca78dc581f0ba39b5a8c62d64 | Jupyter | 33,410 | 1,184 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Testing\n",
"Script for testing basic functionalities that can be better examined visually. How to use: Run the notebook and look at the output."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##... |
4a5559c2de7eed1e7ed2c558b6da07223f15acac6c2a670a2bbad7c64b798e2a | Jupyter | 33,543 | 831 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
c65b516a09b8c8ee485f34f8fc688950df5a3b50a93fbf07d4d0420643fa8496 | Jupyter | 33,903 | 787 | # %%
import numpy as np
from numpy.random import default_rng
import torch
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import TwoSlopeNorm
from matplotlib.ticker import FuncFormatter
from matplotlib.patches import Ellipse
from sklearn.linear_model import Rid... |
af18cc3733640456d088f9af27cd162e6a128e66d435ade4d8dd61c2611b319c | Jupyter | 34,025 | 814 | # %%
import numpy as np
from numpy.random import default_rng
import torch
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import TwoSlopeNorm
from matplotlib.ticker import FuncFormatter
from matplotlib.patches import Ellipse
import matplotlib.transforms as tran... |
db14129a0f25bd45c7b712d331a3a5b20bc14fd047a305d6200a79f2e49e8550 | Jupyter | 34,324 | 699 | # %%
import torch
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler, random_split
import pandas as pd
import numpy as np
import os
import random
from collections import defaultdict, Counter
from itertools import combinations
import json
import torch.nn as nn
import torch.... |
dfef3068b9a983f06c5ba8acf745077e68a435774173e56aa38bfb308c447251 | Jupyter | 34,404 | 706 | # %% [markdown]
# # Machine learning and deep learning methods applied to predicting customer status
#
# Purpose: Predict active and not active clients based on the proposed data structure and naive data structure. Here, we implemented Support Vector Machines (SVM), Random Forest (RF), K-nearest Neighbours (KNN) and L... |
fce85b02d5e5896a051ca8447817af87d6cf8562ef3ac9971a01f7eae4297f44 | Jupyter | 34,633 | 859 | # %%
%load_ext autoreload
%autoreload 2
# %%
import platform
import random
import uuid
import os
import os.path
import skimage
import sak
import sak.signal.wavelet
import sak.data
import sak.data.augmentation
import numpy as np
import scipy as sp
import scipy.signal
import pandas as pd
import networkx
import networkx.... |
fd0dfd7d72422ae6317b5dcd2d48ccdfd1de035ee5f910866784ad542bf9ba66 | Jupyter | 34,694 | 881 | # %%
import math
from sklearn import metrics
from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import time
import datetime
import random
random.seed(1234)
from scipy import interp
import warnings
warnings.filterwarnings("ignore")
from collections import... |
ef651045e5226038363c6dc739dec39e850a0d955a329369b04431f00a4c3a0e | Jupyter | 34,702 | 1,094 | # %%
import sys
import os
from pathlib import Path
module_path = '/usr/scratch/santis/weilenmc/repos'
sys.path.append(module_path)
os.chdir(module_path)
import matplotlib.pyplot as plt
import numpy as np
import json
import pickle
import gym
from gym.wrappers.monitoring.video_recorder import VideoRecorder
import torc... |
0c5e964494d18ce774e995d3130efc9fbead1c8c3efe95406e9a51ec1d709113 | Jupyter | 34,737 | 914 | # %%
import omicverse as ov
from omicverse.utils import mde
import scanpy as sc
import scvelo as scv
ov.utils.ov_plot_set()
# %%
adata=sc.read('data/liver_hpc_anno.h5ad',compression='gzip')
adata
# %%
adata[adata.obs['celltype'].isin(['HPC','GMP','iNP','imNP','mNP','Monocyte','Basophil'])]
# %%
#['HPC','GMP','Erythr... |
a81d2b2145af60a4749d69dd32a3d4232e8668384fb229ff672f3ae32737275e | Jupyter | 34,873 | 710 | # %% [markdown]
# # Machine learning and deep learning methods applied to predicting customer status
#
# Purpose: Predict active and not active clients based on the proposed data structure and naive data structure. Here, we implemented Support Vector Machines (SVM), Random Forest (RF), K-nearest Neighbours (KNN) and L... |
0a704104bfcdc1b64ebaa96f4a3df3d89186418aee0489720b16c1bfe553a1a2 | Jupyter | 34,888 | 795 | # %% [markdown]
# # Examples from manuscript
#
# Code to run the examples reported in https://arxiv.org/abs/2009.01077
# %%
from reval.best_nclust_cv import FindBestClustCV
from reval.internal_baselines import select_best, evaluate_best
from reval.visualization import plot_metrics
from reval.utils import kuhn_munkres... |
0a9cc1fc3a5cc461e90423a58c5cc0e0728a4d92dc106a1730a95fcfd86a96db | Jupyter | 34,907 | 707 | # %% [markdown]
# # Machine learning and deep learning methods applied to predicting customer status
#
# Purpose: Predict active and not active clients based on the proposed data structure and naive data structure. Here, we implemented Support Vector Machines (SVM), Random Forest (RF), K-nearest Neighbours (KNN) and L... |
109b6ae6d0994e40bfd69f0b2508716f5310cfab651e2baaa6bf8401529594de | Jupyter | 34,928 | 1,396 | # %%
import multiprocessing
import tqdm
import numpy as np
import scipy.stats as st
import scipy
import numba
import math
#import biocircuits
# Plotting modules
import bokeh.io
import bokeh.plotting
from bokeh.models import LinearColorMapper, ColorBar
#from bokeh.io import export_svgs
bokeh.io.output_notebook()
# ... |
c085a09e571e17d6d38b96bfebf91faf9deadc918fdb21b5fe67cb9bcff7f72c | Jupyter | 34,969 | 707 | # %% [markdown]
# # Machine learning and deep learning methods applied to predicting customer status
#
# Purpose: Predict active and not active clients based on the proposed data structure and naive data structure. Here, we implemented Support Vector Machines (SVM), Random Forest (RF), K-nearest Neighbours (KNN) and L... |
0ef8139799a8e658729e7be9c8a8e647571c1a1d2217c102ea1a9fe11ac4e10e | Jupyter | 35,375 | 710 | # %% [markdown]
# # Machine learning and deep learning methods applied to predicting customer status
#
# Purpose: Predict active and not active clients based on the proposed data structure and naive data structure. Here, we implemented Support Vector Machines (SVM), Random Forest (RF), K-nearest Neighbours (KNN) and L... |
222b344715e4792924253c2df9cfe830ef776085d579e8f95a0962fabc17a772 | Jupyter | 35,430 | 707 | # %% [markdown]
# # Machine learning and deep learning methods applied to predicting customer status
#
# Purpose: Predict active and not active clients based on the proposed data structure and naive data structure. Here, we implemented Support Vector Machines (SVM), Random Forest (RF), K-nearest Neighbours (KNN) and L... |
972f7d51de9629e4b361ca492d862dd8d0a6e0aba0b5f06b0bde9947652220a8 | Jupyter | 35,620 | 893 | # %% [markdown]
# # init
# %% [markdown]
# ## import package
# %%
import os
import time
from subprocess import call
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
import seaborn as sns
import matplotlib.pyplot as plt
import scipy as sc
from matplotlib.colors import ListedColorma... |
4eac24b6b8fc93b6378f7f1db7343c1b7aa9892ae66842528e53acc3bfc9d006 | Jupyter | 36,086 | 895 | # %% [markdown]
# # Gaussian Processes
# %% [markdown]
# skorch supports integration with the fantastic [GPyTorch](https://gpytorch.ai/) library. GPyTorch implements various Gaussian Process (GP) techniques on top of PyTorch.
# %% [markdown]
# GPyTorch adopts many patterns from PyTorch, thus making it easy to pick up... |
2f860ed2919e941683d535700f9ba202e8cebddaa159a6faf135d5556d5e181e | Jupyter | 36,266 | 783 | # %%
import scanpy as sc
from anndata import read_h5ad
import pandas as pd
import numpy as np
import scipy as sp
from statsmodels.stats.multitest import multipletests
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import os
from os.path import join
import gseapy as gp
import statsmodels.api as ... |
48a056f1e189792051ec9b339692a4c60c6913e51843affa72a77202854b20ff | Jupyter | 36,305 | 941 | # %% [markdown]
# # Overview
# %% [markdown]
# The purpose of this notebook is to conduct batching for samples in preparation for the batched steps of the GATK-SV pipeline.
#
# This notebook performs hierarchical batching as described in the [batching documentation](https://broadinstitute.github.io/gatk-sv/docs/modu... |
f930fd83bfb584b03040fbf93e0472ac1f8d5aa747ba1aeea37cd3efb8926c05 | Jupyter | 36,332 | 1,176 | # %%
import sys
import os
from pathlib import Path
module_path = '/usr/scratch/santis/weilenmc/repos'
sys.path.append(module_path)
os.chdir(module_path)
import matplotlib.pyplot as plt
import numpy as np
import json
import pickle
import gym
from gym.wrappers.monitoring.video_recorder import VideoRecorder
import torc... |
59970d5c0f14fa3d566437307d693c2beeb455c50d8a12e69b78e0f6cf0188d1 | Jupyter | 36,455 | 1,122 | # %% [markdown]
# <h1>Table of Contents<span class="tocSkip"></span></h1>
# <div class="toc"><ul class="toc-item"><li><span><a href="#环境配置" data-toc-modified-id="环境配置-1"><span class="toc-item-num">1 </span>环境配置</a></span></li><li><span><a href="#全局参数设置" data-toc-modified-id="全局参数设置-2"><span class="toc-item-n... |
91dbd969326735732773a314e3e270523185c7372fa0a731acb96c5e4fca1155 | Jupyter | 36,846 | 1,062 | # %% [markdown]
# # LERCause: Causal Sentence Identification with LER (Nuclear Safety Reports)
#
# This code is to run a CNN (Convolutional Neural Networks) model on LER data for sentence classification and prediction.
#
# Author:
# 1. Jinmo Kim: School of Information Sciences, University of Illinoi... |
a4839a97ed4ef9c64d3cdea6a12bd9197cdcc46354afc9b74d05d5c3f71813b3 | Jupyter | 36,881 | 1,134 | # %% [markdown]
# ## __XDF latency analysis of LSL data streams: Unity (triggered) vs EEG (measured)__
# ## Unity 2D project (simplest environment) vs heavy Unity 3D environment
#
# ### __Situation__
# #### Every 500ms a beep sound is played and the background color changes once one frame from black to white.
#
# ##... |
e35a852bae3b0de596761229bd0cddd3108c5318ecbd48c1220775c7846f25d4 | Jupyter | 36,977 | 968 | # %%
import numpy as np
import os
import sys
import nibabel as nib
from pathlib import *
import numpy as np
from scipy import stats as stats
import glob
from scipy.special import xlogy
import numpy as np
import nibabel as nib
#from nilearn import datasets, surface, plotting
from sklearn.decomposition import PCA
from sk... |
79939bc99de12fb7b1ff0945e36f409a7229e5b4c9f030ad7b5f34364a2b66f5 | Jupyter | 37,300 | 909 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
cd2f8bc976d567d2497cec8e4be799d6a19d6d01bdd543836acf733a7aa034ed | Jupyter | 37,372 | 882 | # %% [markdown]
# # Compare Cronbach's alpha to split-halves reliability
#
#
# NOTE
# --
# This part was run on Colab and it was for compatibility reasons as the package pingouin wasn't compatible with packages and versions used in the rest of the paper.
# %% [markdown]
# # Install all needed packages
#
# %%
!pip ... |
cfa874ffd5d9b697aaa53957c6684739fcadf67cd0193bed19f371572c9f1afa | Jupyter | 37,580 | 964 | # %% [markdown]
# # *This file allows to generate all plots necessary for figures*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Polynomials, LaTeXStrings, ColorSchemes, DelimitedFiles, GraphPlot
using Statistics, StatsPlots, Random, ProgressMeter, Printf, LinearAlgebr... |
4c0eea2b406fe3e2cd82cd0fe8a08b43bdeb209bd02d84f7eacca25ea5a22ea3 | Jupyter | 37,595 | 1,064 | # %% [markdown]
# Code created together with Marc Vidal De Palol.
# %% [markdown]
# # Preprocessing: Unify the timestream and create all necessary dfs
# %%
# run to free up memory
%reset
# %% [markdown]
# __Data Saved:__
# %% [markdown]
# Important: participant 8 is saved in two files, they are combined as one in ... |
2125961e18ef4fa372eba7797c59f647609586cd8a9ae1c3da7395def61d6c03 | Jupyter | 37,800 | 933 | # %%
%pip install davos
import davos
%conda install pytables
davos.config.suppress_stdout = True
# %%
smuggle nltools as nlt # pip: nltools==0.4.7
smuggle nilearn as nl # pip: nilearn==0.10.0
smuggle nibabel as nib # pip: nibabel==5.0.1
smuggle datawrangler as dw # pip: pyd... |
409e54a065ce5e87117f37cf11d7b8db042e06762b4844116cae2a0a675ef1c9 | Jupyter | 38,636 | 803 | # %%
import scanpy as sc
from anndata import read_h5ad
import pandas as pd
import numpy as np
import scipy as sp
from statsmodels.stats.multitest import multipletests
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
import os
from os.path import join
import time
from gprofiler import G... |
2651894f572fe181f3db58eac17a072dee4d2765f675feb622bbdf7a83d3fc75 | Jupyter | 38,743 | 1,125 | # %% [markdown]
# # Fine-tuning on Pre-trained Model for Cell-type Annotation
# In this tutorial, we demonstrate how to fine-tune a pre-trained model on a new dataset for the cell type annotation task. We use the Multiple Sclerosis dataset as an example and fine-tune on the pre-trained whole-body model. Please download... |
c2d415e73c2006a822435ac6ff1adef075df5ad674753d5576e8c67f9d87701f | Jupyter | 38,901 | 834 | # %% [markdown]
# # 10 Pre-process data
# %% [markdown]
# Collect data and store it in dataframes and dictionaries
# %% [markdown]
# ## List of dataframes
# * `recordings` Basic data across all recordings from all dendrite
# * `events` Measurements of all synaptic transmission events in the dataset
# * `bursts` Summa... |
0a97e76fb3bccf341a5e1735d410061acaa5443c40e4d301f8db6220376d910c | Jupyter | 39,555 | 899 | # %% [markdown]
# ##### top
# %% [markdown]
# # This ntb is simply processing all the csv files and creating one csv per all conditions
#
# * [Unbiased C](#Unbiased-C)
# * [Estimate C error per all combinations of N and n trials](#Estimate-C-error-per-all-combinations-of-N-and-n-trials)
# * [estimate STD](#STD)
# ... |
f3ddb28df3511184a2195231aa9c82ca962cbb82775bb2607c9ed53f7a5b33dd | Jupyter | 39,837 | 1,203 | # %%
import numpy as np
import pandas as pd
import os
import torch
from torch.autograd import Variable
from matplotlib import pyplot as plt
%matplotlib inline
# %%
import sys
sys.path.append("../Network-SDE-Inference/utils")
import NeuGNN_model
import Self_func
import Interaction_func
from NeuGNN_model import *
from S... |
fd42541d29a682f933f7c5f9555a72859b08305044c216e1279f1abe4065c6b2 | Jupyter | 40,124 | 1,172 | # %%
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV f... |
43fd7fdbe2b9401ea8fde423df80dc5f62df385c120a449c917626580f868aa5 | Jupyter | 40,314 | 1,028 | # %% [markdown]
# # *This file allows to generate all plots necessary for figures*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Polynomials, LaTeXStrings, ColorSchemes, DelimitedFiles, GraphPlot
using Statistics, StatsPlots, Random, ProgressMeter, Printf, LinearAlgebr... |
919a8695006582e503cf77b5be6ac2798634be565ef291e2a919dbdb8b8f1905 | Jupyter | 40,476 | 941 | # %% [markdown]
# ##### top
# %% [markdown]
# # This ntb analyses the Face memory perception task
#
# We have decided that we want to have the following measures for each participant:
#
# For each task (**p**erception, **b**lank, **f**aces, **em**otions) and for each difficulty level (24, 30, 36) we want to have the... |
eb58a893004a4af0962640ed1506bb4d31329e2f8b85315fa58e39e8a10bc592 | Jupyter | 40,875 | 1,451 | # %%
import numpy as np
from sklearn.preprocessing import OneHotEncoder
# %%
# keg2sym={}
# sym2keg={}
# f = open("./whole_Entrez.txt", "r")
# for x in f:
# sym=x.strip().split()[0]
# kegid="hsa:"+x.strip().split()[1]
# keg2sym[kegid]=sym
# sym2keg[sym]=kegid
# g2p={}
# p2g={}
# count=0
# from Bio.KEG... |
84bacaba41ca57656e2e94dea2c1f0e273d76601c023294b6c49e934e85dda95 | Jupyter | 41,081 | 995 | # %%
import datetime
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
import tensorflow as tf
from tensorflow import keras
import keras.backend as K
import os
import sys
import h5py
import math
import csv
import pickle
import glob
import statistics
!pip install detecta
from detecta i... |
63670d7197f5372a6f38ec629e50cc4348bd5a34878e8a76faf049d36c98ebd9 | Jupyter | 41,566 | 1,131 | # %%
# %matplotlib widget
# %%
import flammkuchen as fl
from matplotlib import pyplot as plt
import numpy as np
from scipy import io
import os
import seaborn as sns
import matplotlib.gridspec as gridspec
from matplotlib.ticker import FormatStrFormatter
from pathlib import Path
import pandas as pd
from luminance_analy... |
fbd96c07bb6180a2268c779de9529a3931a11eb196608e72faf517046f72f694 | Jupyter | 41,808 | 801 | # %%
#import packages
import vedo
import numpy as np
import pandas as pd
import json
import glob
import os
import networkx as nx
import ast
from nxviz import utils
from operator import itemgetter
from skimage import img_as_uint, exposure
from skimage.io import imread
import warnings
warnings.filterwarnings('ignore')
#... |
b4bdee38ab488e3edd0c47a6708ee9bf026f6ccdf0ba060abf55c9ed7a348a52 | Jupyter | 42,057 | 408 | # %% [markdown]
# # Generate the depolymerase DB based on Interproscan entries.
# ***
# #### The multi fasta files of the intersting entries have already been downloaded. Here are the steps to follow :
# ### I. Create a rep for each IPR entry
# ### II. Cluster each entry using mmseqs
# ### III. Generate the required fi... |
4ef73a83542c52839f17a47b00ad76c21e756cc67fd95d92cda6a9798accb41b | Jupyter | 42,064 | 411 | # %% [markdown]
# # Generate the depolymerase DB based on Interproscan entries.
# ***
# #### The multi fasta files of the intersting entries have already been downloaded. Here are the steps to follow :
# ### I. Create a rep for each IPR entry
# ### II. Cluster each entry using mmseqs
# ### III. Generate the required fi... |
14ebc13b4279ac976ce21ef8cf25a401741c2ab0ef67f5d36600f82a318c405c | Jupyter | 42,142 | 1,110 | # %%
import warnings
warnings.filterwarnings("ignore")
import os
import math
import time
import gzip
import pickle
import numpy as np
from pathlib import Path
from copy import deepcopy
from time import strftime
from functools import partial
from skimage import io as skio
from scipy.ndimage import binary_fill_holes
fro... |
7295645db6feb8efbec896a989af447af341f206fb2f0a079c0d11d7a70e9640 | Jupyter | 42,512 | 1,191 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
ae28b2fe5f05e594bb47aaad353b3100d75d3c8afffe54d1a437bd9952183ba2 | Jupyter | 42,663 | 1,238 | # %% [markdown]
# # LERCause: Causal Sentence Identification with LER (Nuclear Safety Reports)
#
# This code is to run pre-trained language models (BERT, BioBERT, SciBERT) on LER data for sentence classification and prediction.
#
# Author:
# 1. Jinmo Kim: School of Information Sciences, University of Illinois Ur... |
1fe05a20b86e1ba206cffedb18849ceef36f6cd68ec08ea7305d911390b58531 | Jupyter | 42,731 | 988 | # %% [markdown]
# <a href="https://colab.research.google.com/github/bermanlabemory/motionmapperpy/blob/master/demo/motionmapperpy_fly_demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# The github repositories for motionmapper are... |
b9894d05bae9542ad16936de3c1e70761f38068e27aae65b22ecc3ec1efd1bd1 | Jupyter | 42,745 | 1,068 | # %%
#| default_exp __init__
# %%
#| export
import warnings
from types import SimpleNamespace
from typing import Union, Optional
from inspect import signature
from math import nan, sqrt, log, log2, pi, ceil
import cmath
import numpy as np
from mne.utils import logger, verbose
from mne.io.base import BaseRaw
from mn... |
79c7c4f43697cca070db4bdd1a26a344b453bba7fe8cfe7f9341641ce5d933c2 | Jupyter | 45,044 | 1,284 | # %%
%load_ext autoreload
%autoreload 2
# %%
import platform
import random
import uuid
import os
import os.path
import skimage
import sak
import sak.signal.wavelet
import sak.data
import sak.data.augmentation
import numpy as np
import scipy as sp
import scipy.signal
import pandas as pd
import networkx
import networkx.... |
c332dee3a08725dcce4c6542927d92ede321dd01fe8536fb669200ad8ecb96f4 | Jupyter | 45,388 | 1,265 | # %%
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/... |
0cf38db578cf9414d1b845b8501b0aa95ffc435683dc58a97e1b8949da9349b7 | Jupyter | 45,594 | 795 | # %% [markdown]
# # Imports
# %%
proj_dir = "/path/to/main_project_folder/" # edit this line
import numpy as np
import random
import xarray as xr
import time
import h5py
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow.random import set_seed as tf_set_seed
import tensorflow.compat.v1 as tf_compat_v... |
1d11e505b5ddb8ea74bd52ceb27fd36a77e604bc1310f6a73484bcc2993e8b61 | Jupyter | 46,323 | 1,389 | # %% [markdown]
# ## __XDF latency analysis of LSL data streams: Unity (triggered) vs EEG (measured)__
#
# ### __Situation__
# #### Every 500ms a beep sound is played and the background color changes once one frame from black to white.
#
# #### __Unity (90 FPS):__
# - Color change (black or white background)
# - Bee... |
427b391bfcc03e48fddb4eeea051daff399a23b696dfab007388ef55214325af | Jupyter | 46,639 | 1,099 | # %% [markdown]
# ### get the consensus motifs from retrained Base_model1_10000, 9000 and 8000; get the consensus motifs from Base_model2_10000, 9000 and 8000 and compare the consensus motifs from the two model structures
# %%
import torch
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader, S... |
44b33e290436426aedd55685f15e2c33a94c55cf6b83d05d667cc028e9d80e1b | Jupyter | 46,900 | 979 | # %%
import os
import math
import operator
from collections import defaultdict
import numpy as np
import pandas as pd
from scipy import stats
import statsmodels
import statsmodels.formula.api as smf
from statsmodels.multivariate.manova import MANOVA
from statsmodels.tools.sm_exceptions import PerfectSeparationError
fr... |
e5b5d340aba53f90e80eb3210051163aee924fabc5c59df3127a1a776cfca4cf | Jupyter | 47,597 | 1,153 | # %% [markdown]
# # Notebook: 2-supervised-classification
#
# Accompanying the paper:<br>
# A. J. Lundervold, ..., A. Lundervold. _Decoding IBS: A Machine Learning Approach to Psychological Distress and Gut-Brain Interaction_
#
# Arvid Lundervold, v. 2024-07-21 - using conda kernel `ibs` (Python 3.11.9))
#
# Last ... |
0eff6fcf767052ea41feee3bebef2b32c33c648361a03148a90754e3737b660d | Jupyter | 48,125 | 1,085 | # %%
#import packages
import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
import scipy.stats as stats
import networkx as nx
from operator import itemgetter
from networkx.algorithms import community
from nxviz import utils
from tqdm import tqdm
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams... |
c9d46fbed6db667521f7a1d203c22bdfe2cf8f1a76a2f12831abc2c8de6af2d0 | Jupyter | 49,096 | 1,107 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
306e1c575210ae78e41014d4120519ad1f8434c269f1acb776e1b3b008be264f | Jupyter | 49,153 | 1,126 | # %% [markdown]
# ##### top
# %% [markdown]
# # This ntb analyses the Face memory perception task
#
# We have decided that we want to have the following measures for each participant:
#
# For each task (**p**erception, **b**lank, **f**aces, **em**otions) and for each difficulty level (24, 30, 36) we want to have the... |
2902946810c7b8ea941c9a9980512063174c4283dd1a2d587a7bc9bf7d1aa66d | Jupyter | 49,363 | 1,166 | # %% [markdown]
# # Figure 3: Inactivation
# %%
# Imports
## General
import os
from copy import deepcopy
from itertools import product
from types import SimpleNamespace
import sys
sys.path.append("..")
from RNN_helpers import *
from IPython.display import display, Markdown
## Numerical
import numpy as np
import pan... |
96d25433530f4559d514165c91e55f49fca394868a26d16903493e83588f4387 | Jupyter | 49,548 | 1,207 | # %% [markdown]
# <a href="https://colab.research.google.com/github/michaelhess17/motionmapperpy/blob/master/demo/motionmapperpy_mouse_demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# The github repositories for motionmapper ar... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.