markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
Directory to store Models | import os
if not os.path.exists('./models'):
os.mkdir('./models')
def position_index(x):
if x<4:
return 1
if x>10:
return 3
else :
return 2 | _____no_output_____ | UPL-1.0 | beginners/04.ML_Modelling.ipynb | MKulfan/redbull-analytics-hol |
Model considering only Drivers | x_d= data[['GP_name','quali_pos','driver','age_at_gp_in_days','position','driver_confidence','active_driver']]
x_d = x_d[x_d['active_driver']==1]
sc = StandardScaler()
le = LabelEncoder()
x_d['GP_name'] = le.fit_transform(x_d['GP_name'])
x_d['driver'] = le.fit_transform(x_d['driver'])
x_d['GP_name'] = le.fit_transform... | _____no_output_____ | UPL-1.0 | beginners/04.ML_Modelling.ipynb | MKulfan/redbull-analytics-hol |
Model considering only Constructors | x_c = data[['GP_name','quali_pos','constructor','position','constructor_reliability','active_constructor']]
x_c = x_c[x_c['active_constructor']==1]
sc = StandardScaler()
le = LabelEncoder()
x_c['GP_name'] = le.fit_transform(x_c['GP_name'])
x_c['constructor'] = le.fit_transform(x_c['constructor'])
X_c = x_c.drop(['posi... | _____no_output_____ | UPL-1.0 | beginners/04.ML_Modelling.ipynb | MKulfan/redbull-analytics-hol |
Model considering both Drivers and Constructors | cleaned_data = data[['GP_name','quali_pos','constructor','driver','position','driver_confidence','constructor_reliability','active_driver','active_constructor']]
cleaned_data = cleaned_data[(cleaned_data['active_driver']==1)&(cleaned_data['active_constructor']==1)]
cleaned_data.to_csv('./data_f1/cleaned_data.csv',index... | _____no_output_____ | UPL-1.0 | beginners/04.ML_Modelling.ipynb | MKulfan/redbull-analytics-hol |
Build your X dataset with next columns:- GP_name- quali_pos to predict the classification cluster (1,2,3) - constructor- driver- position- driver confidence- constructor_reliability- active_driver- active_constructor Filter the dataset for this Model "Driver + Constructor" all active drivers and constructors Create ... | # Implement X, y | _____no_output_____ | UPL-1.0 | beginners/04.ML_Modelling.ipynb | MKulfan/redbull-analytics-hol |
Applied the same list of ML Algorithms for cross validation of different modelsAnd Store the accuracy Mean Value in order to compare with previous ML Models | mean_results = []
results = []
name = []
# cross validation for different models
| _____no_output_____ | UPL-1.0 | beginners/04.ML_Modelling.ipynb | MKulfan/redbull-analytics-hol |
Use the same boxplot plotter used in the previous Models | # Implement boxplot
| _____no_output_____ | UPL-1.0 | beginners/04.ML_Modelling.ipynb | MKulfan/redbull-analytics-hol |
Comparing The 3 ML ModelsLet's see mean score of our three assumptions. | lr = [mean_results[0],mean_results_dri[0],mean_results_const[0]]
dtc = [mean_results[1],mean_results_dri[1],mean_results_const[1]]
rfc = [mean_results[2],mean_results_dri[2],mean_results_const[2]]
svc = [mean_results[3],mean_results_dri[3],mean_results_const[3]]
gnb = [mean_results[4],mean_results_dri[4],mean_results_c... | 62.024924516677856 seconds
| UPL-1.0 | beginners/04.ML_Modelling.ipynb | MKulfan/redbull-analytics-hol |
Model buildinghttps://www.kaggle.com/vadbeg/pytorch-nn-with-embeddings-and-catboost/notebookPyTorchmostly based off this example, plus parts of code form tutorial 5 lab 3 | # import load_data function from
%load_ext autoreload
%autoreload 2
# fix system path
import sys
sys.path.append("/home/jovyan/work")
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
import random
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
... | /opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:24: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
| FTL | notebooks/Model Building.ipynb | Reasmey/adsi_beer_app |
forgot to divide the loss and accuracy by length of data set | print('Training Accuracy: {:.2f}%'.format(5926.0/300.0))
print('Validation Accuracy: {:.2f}%'.format(2361.0/300.0)) | Training Accuracy: 19.75%
Validation Accuracy: 7.87%
| FTL | notebooks/Model Building.ipynb | Reasmey/adsi_beer_app |
Predict with test set | def predict(data_loader, model):
model.eval()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model.to(device)
with torch.no_grad():
predictions = None
for i, batch in enumerate(tqdm(data_loader)):
output = model(batc... | _____no_output_____ | FTL | notebooks/Model Building.ipynb | Reasmey/adsi_beer_app |
* 比较不同组合组合优化器在不同规模问题上的性能；* 下面的结果主要比较``alphamind``和``python``中其他优化器的性能差别，我们将尽可能使用``cvxopt``中的优化器，其次选择``scipy``；* 由于``scipy``在``ashare_ex``上面性能太差，所以一般忽略``scipy``在这个股票池上的表现；* 时间单位都是毫秒。* 请在环境变量中设置`DB_URI`指向数据库 | import os
import timeit
import numpy as np
import pandas as pd
import cvxpy
from alphamind.api import *
from alphamind.portfolio.linearbuilder import linear_builder
from alphamind.portfolio.meanvariancebuilder import mean_variance_builder
from alphamind.portfolio.meanvariancebuilder import target_vol_builder
pd.option... | _____no_output_____ | MIT | notebooks/Example 7 - Portfolio Optimizer Performance.ipynb | wangjiehui11235/alpha-mind |
0. 数据准备------------------ | ref_date = '2018-02-08'
u_names = ['sh50', 'hs300', 'zz500', 'zz800', 'zz1000', 'ashare_ex']
b_codes = [16, 300, 905, 906, 852, None]
risk_model = 'short'
factor = 'EPS'
lb = 0.0
ub = 0.1
data_source = os.environ['DB_URI']
engine = SqlEngine(data_source)
universes = [Universe(u_name) for u_name in u_names]
codes_set =... | _____no_output_____ | MIT | notebooks/Example 7 - Portfolio Optimizer Performance.ipynb | wangjiehui11235/alpha-mind |
1. 线性优化（带线性限制条件）--------------------------------- | df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])
number = 1
for u_name, sample_data in zip(u_names, data_set):
factor_data = sample_data['factor']
er = factor_data[factor].values
n = len(er)
lbound = np.ones(n) * lb
ubound = np.ones(n) * ub
risk_constraints = np.ones((n, 1... | _____no_output_____ | MIT | notebooks/Example 7 - Portfolio Optimizer Performance.ipynb | wangjiehui11235/alpha-mind |
2. 线性优化（带L1限制条件）----------------------- | from cvxpy import pnorm
df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind (clp simplex)', 'alphamind (clp interior)', 'alphamind (ecos)'])
turn_over_target = 0.5
number = 1
for u_name, sample_data in zip(u_names, data_set):
factor_data = sample_data['factor']
er = factor_data[factor].values
n ... | _____no_output_____ | MIT | notebooks/Example 7 - Portfolio Optimizer Performance.ipynb | wangjiehui11235/alpha-mind |
3. Mean - Variance 优化 （无约束）----------------------- | from cvxpy import *
df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])
number = 1
for u_name, sample_data in zip(u_names, data_set):
all_styles = risk_styles + industry_styles + ['COUNTRY']
factor_data = sample_data['factor']
risk_cov = sample_data['risk_cov'][all_styles].values
risk_exp... | _____no_output_____ | MIT | notebooks/Example 7 - Portfolio Optimizer Performance.ipynb | wangjiehui11235/alpha-mind |
4. Mean - Variance 优化 （Box约束）--------------- | df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])
number = 1
for u_name, sample_data in zip(u_names, data_set):
all_styles = risk_styles + industry_styles + ['COUNTRY']
factor_data = sample_data['factor']
risk_cov = sample_data['risk_cov'][all_styles].values
risk_exposure = factor_data[a... | _____no_output_____ | MIT | notebooks/Example 7 - Portfolio Optimizer Performance.ipynb | wangjiehui11235/alpha-mind |
5. Mean - Variance 优化 （Box约束以及线性约束）---------------- | df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])
number = 1
for u_name, sample_data in zip(u_names, data_set):
all_styles = risk_styles + industry_styles + ['COUNTRY']
factor_data = sample_data['factor']
risk_cov = sample_data['risk_cov'][all_styles].values
risk_exposure = factor_data[a... | _____no_output_____ | MIT | notebooks/Example 7 - Portfolio Optimizer Performance.ipynb | wangjiehui11235/alpha-mind |
6. 线性优化（带二次限制条件）------------------------- | df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])
number = 1
target_vol = 0.5
for u_name, sample_data in zip(u_names, data_set):
all_styles = risk_styles + industry_styles + ['COUNTRY']
factor_data = sample_data['factor']
risk_cov = sample_data['risk_cov'][all_styles].values
risk_exposu... | _____no_output_____ | MIT | notebooks/Example 7 - Portfolio Optimizer Performance.ipynb | wangjiehui11235/alpha-mind |
Based on **Train-AEmodel-GRU2x32-encoding16-AEmodel-DR5-ps-SDSS-QSO-balanced-wandb.ipynb** To-do | gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
print('Select the Runtime > "Change runtime type" menu to enable a GPU accelerator, ')
print('and then re-execute this cell.')
else:
print(gpu_info)
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print(... | Your runtime has 8.6 gigabytes of available RAM
To enable a high-RAM runtime, select the Runtime > "Change runtime type"
menu, and then select High-RAM in the Runtime shape dropdown. Then,
re-execute this cell.
| Apache-2.0 | 01_(Paula)TrainAE.ipynb | hernanlira/hl_stargaze |
Pixel ShuffleThis notebook is a comparison between two best practices. Pixel shuffle and upsampling followed by a convolution. Imports | from fastai import *
from fastai.tabular import *
import pandas as pd
from torchsummary import summary
import torch
from torch import nn
import imageio
import torch
import glob
from fastai.vision import *
import os
from torch import nn
import torch.nn.functional as F | _____no_output_____ | MIT | notebooks/cifar-10/pixelShuffle.ipynb | henriwoodcock/Applying-Modern-Best-Practices-to-Autoencoders |
Data | colab = True
if colab:
from google.colab import drive
drive.mount('/content/drive', force_remount = True)
%cp "/content/drive/My Drive/autoencoder-training/data.zip" .
!unzip -q data.zip
image_path = "data"
%cp "/content/drive/My Drive/autoencoder-training/model_layers.py" .
%cp "/content/drive/My Drive/a... | _____no_output_____ | MIT | notebooks/cifar-10/pixelShuffle.ipynb | henriwoodcock/Applying-Modern-Best-Practices-to-Autoencoders |
Model | autoencoder = pixelShuffle_model.autoencoder()
learn = Learner(data, autoencoder, loss_func = F.mse_loss)
learn.fit_one_cycle(5)
learn.lr_find()
learn.recorder.plot(suggestion=True)
learn.metrics = [mean_squared_error, mean_absolute_error, r2_score, explained_variance]
learn.fit_one_cycle(10, max_lr = 1e-03) | _____no_output_____ | MIT | notebooks/cifar-10/pixelShuffle.ipynb | henriwoodcock/Applying-Modern-Best-Practices-to-Autoencoders |
Results Training | learn.show_results(ds_type=DatasetType.Train) | _____no_output_____ | MIT | notebooks/cifar-10/pixelShuffle.ipynb | henriwoodcock/Applying-Modern-Best-Practices-to-Autoencoders |
Validation | learn.show_results(ds_type=DatasetType.Valid)
torch.save(autoencoder, "/content/drive/My Drive/autoencoder-training/pixelShuffle-Cifar10.pt") | _____no_output_____ | MIT | notebooks/cifar-10/pixelShuffle.ipynb | henriwoodcock/Applying-Modern-Best-Practices-to-Autoencoders |
y_label = np.argmax(y_data, axis=1)y_text = ['bed', 'bird', 'cat', 'dog', 'house', 'tree']y_table = {i:text for i, text in enumerate(y_text)}y_table_array = np.array([(i, text) for i, text in enumerate(y_text)]) x_train_temp, x_test, y_train_temp, y_test = train_test_split( x_2d_data, y_label, test_size=0.2, random_... | np.savez_compressed(path.join(base_path, 'imagenet_6_class_172_train_data_1.npz'),
x_data=x_train, y_data=y_train, y_list=y_list)
np.savez_compressed(path.join(base_path, 'imagenet_6_class_172_val_data_1.npz'),
x_data=x_val, y_data=y_val, y_list=y_list) | _____no_output_____ | MIT | make_172_imagenet_6_class_data-Copy1.ipynb | BbChip0103/research_2d_bspl |
Control Flow Python if else | def multiply(a, b):
"""Function to multiply"""
print(a * b)
print(multiply.__doc__)
multiply(5,2)
def func():
"""Function to check i is greater or smaller"""
i=10
if i>5:
print("i is greater than 5")
else:
print("i is less than 15")
print(func.__doc__)
func() | Function to check i is greater or smaller
i is greater than 5
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Nested if | if i==20:
print("i is 10")
if i<15:
print("i is less than 15")
if i>15:
print("i is greater than 15")
else:
print("Not present") | _____no_output_____ | MIT | 03...learn_python.ipynb | ram574/Python-Learning |
if-elif-else ladder | def func():
i=10
if i==10:
print("i is equal to 10")
elif i==15:
print("Not present")
elif i==20:
print('i am there')
else:
print("none")
func() | _____no_output_____ | MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Python for loop | def func():
var = input("enter number:")
x = int(var)
for i in range(x):
print(i)
func()
## Lists iteration
def func():
print("List Iteration")
l = ["tulasi", "ram", "ponaganti"]
for i in l:
print(i)
func()
# Iterating over a tuple (immutable)
def func():
print("\nTuple Iter... | List Iteration
tulasi
ram
ponaganti
Tuple Iteration
tulasi
ram
ponaganti
String Iteration
t
u
l
a
s
i
Dictionary Iteration
xyz 123
abc 345
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Python for Loop with Continue Statement | def func():
for letter in 'tulasiram':
if letter == 'a':
continue
print(letter)
func() | t
u
l
s
i
r
m
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Python For Loop with Break Statement | def func():
for letter in 'tulasiram':
if letter == 'a':
break
print('Current Letter :', letter)
func() | Current Letter : t
Current Letter : u
Current Letter : l
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Python For Loop with Pass Statement | list = ['tulasi','ram','ponaganti']
def func():
#An empty loop
for list in 'ponaganti':
pass
print('Last Letter :', list)
func() | Last Letter : i
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Python range | def func():
sum=0
for i in range(1,5):
sum = sum + i
print(sum)
func()
def func():
i=5
for x in range(i):
i = i+x
print(i)
func() | 5
6
8
11
15
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Python for loop with else | for i in range(1, 4):
print(i)
else: # Executed because no break in for
print("No Break\n")
for i in range(1, 4):
print(i)
break
else: # Not executed as there is a break
print("No Break")
### Using all for loop statements in small program
def func():
var = input("enter number:")
x = int(va... | 1 * 1 = 1
1 * 2 = 2
1 * 3 = 3
1 * 4 = 4
1 * 5 = 5
1 * 6 = 6
1 * 7 = 7
1 * 8 = 8
1 * 9 = 9
1 * 10 = 10
1 * 11 = 11
1 * 12 = 12
2 * 1 = 2
2 * 2 = 4
2 * 3 = 6
2 * 4 = 8
2 * 5 = 10
2 * 6 = 12
2 * 7 = 14
2 * 8 = 16
2 * 9 = 18
2 * 10 = 20
2 * 11 = 22
2 * 12 = 24
3 * 1 = 3
3 * 2 = 6
3 * 3 = 9
3 * 4 = 12
3 * 5 = 15
3 * 6 = 18
... | MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Python while loop | ## Single line statement
def func():
'''first one'''
count = 0
while (count < 5): count = count + 1; print("Tulasi Ram")
print(func.__doc__)
func()
### or
def func():
'''Second one'''
count = 0
while (count < 5):
count = count + 1
print("Tulasi Ram")
print(func.__doc__)
func... | 1
2
3
4
5
6
7
8
9
10
no break
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
using break in loops | def func():
i=0
for i in range(10):
i+=1
print(i)
break
else:
print('no break')
func() | 1
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
using continue in loops | def func():
i=0
for i in range(10):
i+=1
print(i)
continue
else:
for i in range(5):
i+=1
print(i)
break
func()
def func():
i=0
for i in range(10):
i+=1
print(i)
pass
else:
for i in range(... | 1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Looping techniques using enumerate() | def enumearteFunc():
list =['tulasi','ram','ponaganti']
for key in enumerate(list):
print(key)
enumearteFunc()
def enumearteFunc():
list =['tulasi','ram','ponaganti']
for key, value in enumerate(list):
print(value)
enumearteFunc()
def zipFunc():
list1 = ['name', 'firstname', 'lastna... | What is your name? I am ram.
What is your firstname? I am tulasi.
What is your lastname? I am ponaganti.
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
""" Using iteritem(): iteritems() is used to loop through the dictionary printing the dictionary key-value pair sequentially which is used before Python 3 version Using items(): items() performs the similar task on dictionary as iteritems() but have certain disadvantages when compared with iteritems() "... | def itemFunc():
name = {"name": "tulasi", "firstname": "ram"}
print("The key value pair using items is : ")
for key, value in name.items():
print(key, value)
itemFunc() | The key value pair using items is :
name tulasi
firstname ram
| MIT | 03...learn_python.ipynb | ram574/Python-Learning |
sorting the list items using loop | def sortedFunc():
list = ['ram','tulasi','ponaganti']
for i in list:
print(sorted(i))
continue
for i in reversed(list):
print(i, end=" ")
sortedFunc() | ['a', 'm', 'r']
['a', 'i', 'l', 's', 't', 'u']
['a', 'a', 'g', 'i', 'n', 'n', 'o', 'p', 't']
ponaganti tulasi ram | MIT | 03...learn_python.ipynb | ram574/Python-Learning |
Load CNNTracker | #only need to select one model
#Model 1 CNN tracker for ICA TOF MRA
swc_name = 'cnn_snake'
import sys
sys.path.append(r'U:\LiChen\AICafe\CNNTracker')
from models.centerline_net import CenterlineNet
max_points = 500
prob_thr = 0.85
infer_model = CenterlineNet(n_classes=max_points)
checkpoint_path_infer = r"D:\tensorf... | _____no_output_____ | MIT | CNNTracker1-2.ipynb | clatfd/Coronary-Artery-Tracking-via-3D-CNN-Classification |
Load datasets | dbname = 'BRAVEAI'
icafe_dir = r'\\DESKTOP2\GiCafe\result/'
seg_model_name = 'LumenSeg2-3'
with open(icafe_dir+'/'+dbname+'/db.list','rb') as fp:
dblist = pickle.load(fp)
train_list = dblist['train']
val_list = dblist['val']
test_list = dblist['test']
pilist = [pi.split('/')[1] for pi in dblist['test']]
len(pilist... | _____no_output_____ | MIT | CNNTracker1-2.ipynb | clatfd/Coronary-Artery-Tracking-via-3D-CNN-Classification |
Tracking | # from s.whole.modelname to swc traces
from iCafePython.connect.ext import extSnake
import SimpleITK as sitk
#redo artery tracing
RETRACE = 1
#redo artery tree contraint
RETREE = 1
#segmentation src
seg_src = 's.whole.'+seg_model_name
#Lumen segmentation threshold.
# Lower value will cause too many noise branches, ... | _____no_output_____ | MIT | CNNTracker1-2.ipynb | clatfd/Coronary-Artery-Tracking-via-3D-CNN-Classification |
Artery labeling | from iCafePython.artlabel.artlabel import ArtLabel
art_label_predictor = ArtLabel()
for pi in pilist[:]:
print('='*10,'Start processing',pilist.index(pi),'/',len(pilist),pi,'='*10)
if not os.path.exists(icafe_dir+'/'+dbname+'/'+pi):
os.mkdir(icafe_dir+'/'+dbname+'/'+pi)
icafem = iCafe(icafe... | _____no_output_____ | MIT | CNNTracker1-2.ipynb | clatfd/Coronary-Artery-Tracking-via-3D-CNN-Classification |
Eval | def eval_simple(snakelist):
snakelist = copy.deepcopy(snakelist)
_ = snakelist.resampleSnakes(1)
#ground truth snakelist from icafem.veslist
all_metic = snakelist.motMetric(icafem.veslist)
metric_dict = all_metic.metrics(['MOTA','IDF1','MOTP','IDS'])
#ref_snakelist = icafem.readSnake('ves')
... | _____no_output_____ | MIT | CNNTracker1-2.ipynb | clatfd/Coronary-Artery-Tracking-via-3D-CNN-Classification |
Assignment of Day 5 | lst1 = [1,5,6,4,1,2,3,5]
lst2 = [1,5,6,5,1,2,3,6]
lst = [1,1,5]
count = 0
r=0
for x in lst:
for y in lst1[r:]:
r+=1
if (x==y):
count+=1
break;
else:
pass
if(count==3):
print("it’s a Match")
else:
print("it’s Gone")
count = 0
r=0
for x in... | ['HEY THIS IS SAI', 'I AM IN MUMBAI', '....']
| Apache-2.0 | Assignment day5.ipynb | Raghavstyleking/LetsUpgrade-Python-Essentials |
Day and Night Image Classifier---The day/night image dataset consists of 200 RGB color images in two categories: day and night. There are equal numbers of each example: 100 day images and 100 night images.We'd like to build a classifier that can accurately label these images as day or night, and that relies on finding... | import cv2 # computer vision library
import helpers
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline | _____no_output_____ | MIT | 1_1_Image_Representation/6_3. Average Brightness.ipynb | georgiagn/CVND_Exercises |
Training and Testing DataThe 200 day/night images are separated into training and testing datasets. * 60% of these images are training images, for you to use as you create a classifier.* 40% are test images, which will be used to test the accuracy of your classifier.First, we set some variables to keep track of some w... | # Image data directories
image_dir_training = "day_night_images/training/"
image_dir_test = "day_night_images/test/" | _____no_output_____ | MIT | 1_1_Image_Representation/6_3. Average Brightness.ipynb | georgiagn/CVND_Exercises |
Load the datasetsThese first few lines of code will load the training day/night images and store all of them in a variable, `IMAGE_LIST`. This list contains the images and their associated label ("day" or "night"). For example, the first image-label pair in `IMAGE_LIST` can be accessed by index: ``` IMAGE_LIST[0][:]``... | # Using the load_dataset function in helpers.py
# Load training data
IMAGE_LIST = helpers.load_dataset(image_dir_training)
| _____no_output_____ | MIT | 1_1_Image_Representation/6_3. Average Brightness.ipynb | georgiagn/CVND_Exercises |
Construct a `STANDARDIZED_LIST` of input images and output labels.This function takes in a list of image-label pairs and outputs a **standardized** list of resized images and numerical labels. | # Standardize all training images
STANDARDIZED_LIST = helpers.standardize(IMAGE_LIST) | _____no_output_____ | MIT | 1_1_Image_Representation/6_3. Average Brightness.ipynb | georgiagn/CVND_Exercises |
Visualize the standardized dataDisplay a standardized image from STANDARDIZED_LIST. | # Display a standardized image and its label
# Select an image by index
image_num = 0
selected_image = STANDARDIZED_LIST[image_num][0]
selected_label = STANDARDIZED_LIST[image_num][1]
# Display image and data about it
plt.imshow(selected_image)
print("Shape: "+str(selected_image.shape))
print("Label [1 = day, 0 = nig... | Shape: (600, 1100, 3)
Label [1 = day, 0 = night]: 1
| MIT | 1_1_Image_Representation/6_3. Average Brightness.ipynb | georgiagn/CVND_Exercises |
Feature ExtractionCreate a feature that represents the brightness in an image. We'll be extracting the **average brightness** using HSV colorspace. Specifically, we'll use the V channel (a measure of brightness), add up the pixel values in the V channel, then divide that sum by the area of the image to get the average... | # Convert and image to HSV colorspace
# Visualize the individual color channels
image_num = 0
test_im = STANDARDIZED_LIST[image_num][0]
test_label = STANDARDIZED_LIST[image_num][1]
# Convert to HSV
hsv = cv2.cvtColor(test_im, cv2.COLOR_RGB2HSV)
# Print image label
print('Label: ' + str(test_label))
# HSV channels
h... | Label: 1
| MIT | 1_1_Image_Representation/6_3. Average Brightness.ipynb | georgiagn/CVND_Exercises |
--- Find the average brightness using the V channelThis function takes in a **standardized** RGB image and returns a feature (a single value) that represent the average level of brightness in the image. We'll use this value to classify the image as day or night. | # Find the average Value or brightness of an image
def avg_brightness(rgb_image):
# Convert image to HSV
hsv = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2HSV)
# Add up all the pixel values in the V channel
sum_brightness = np.sum(hsv[:,:,2])
## TODO: Calculate the average brightness using the ... | Avg brightness: 35.217
| MIT | 1_1_Image_Representation/6_3. Average Brightness.ipynb | georgiagn/CVND_Exercises |
Lab Three---For this lab we're going to be making and using a bunch of functions. Our Goals are:- Switch Case- Looping- Making our own functions- Combining functions- Structuring solutions | // Give me an example of you using switch case.
String house = "BlueLions";
switch(house){
case "BlueLions":
System.out.println("Dimitri");
case "BlackEagles":
System.put.println("Edelgard");
case "GoldenDeer":
System.out.println("Claude");
}
// Give me an example of you using a for... | _____no_output_____ | MIT | JupyterNotebooks/Labs/Lab 3.ipynb | CometSmudge/CMPT-220L-903-21S |
class test:
def __init__(self,a):
self.a=a
def display(self):
print(self.a)
obj=test()
obj.display()
def f1():
x=100
print(x)
x=+1
f1()
area = { 'living' : [400, 450], 'living' : [650, 800], 'kitchen' : [300, 250], 'garage' : [250, 0]}
print (area['living'])
List_1=[2,6,7,8]
List_2=[2,6,7,8... | _____no_output_____ | MIT | practice_project.ipynb | Abhishekauti21/dsmp-pre-work | |
Detecting COVID-19 with Chest X Ray using PyTorchImage classification of Chest X Rays in one of three classes: Normal, Viral Pneumonia, COVID-19Dataset from [COVID-19 Radiography Dataset](https://www.kaggle.com/tawsifurrahman/covid19-radiography-database) on Kaggle Importing Libraries | from google.colab import drive
drive.mount('/gdrive')
%matplotlib inline
import os
import shutil
import copy
import random
import torch
import torch.nn as nn
import torchvision
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import seaborn as sns
import time
from sklearn.metrics imp... | Using PyTorch version 1.7.0+cu101
| Apache-2.0 | Model/Resnet_18.ipynb | reyvnth/COVIDX |
Preparing Training and Test Sets | class_names = ['Non-Covid', 'Covid']
root_dir = '/gdrive/My Drive/Research_Documents_completed/Data/Data/'
source_dirs = ['non', 'covid'] | _____no_output_____ | Apache-2.0 | Model/Resnet_18.ipynb | reyvnth/COVIDX |
Creating Custom Dataset | class ChestXRayDataset(torch.utils.data.Dataset):
def __init__(self, image_dirs, transform):
def get_images(class_name):
images = [x for x in os.listdir(image_dirs[class_name]) if x.lower().endswith('png') or x.lower().endswith('jpg')]
print(f'Found {len(images)} {class_name} example... | _____no_output_____ | Apache-2.0 | Model/Resnet_18.ipynb | reyvnth/COVIDX |
Image Transformations | train_transform = torchvision.transforms.Compose([
torchvision.transforms.Resize(size=(224, 224)),
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
test_transform = torchvision.tr... | _____no_output_____ | Apache-2.0 | Model/Resnet_18.ipynb | reyvnth/COVIDX |
Prepare DataLoader | train_dirs = {
'Non-Covid': '/gdrive/My Drive/Research_Documents_completed/Data/Data/non/',
'Covid': '/gdrive/My Drive/Research_Documents_completed/Data/Data/covid/'
}
#train_dirs = {
# 'Non-Covid': '/gdrive/My Drive/Data/Data/non/',
# 'Covid': '/gdrive/My Drive/Data/Data/covid/'
#}
train_dataset = Chest... | <torch.utils.data.dataloader.DataLoader object at 0x7f3c11961048>
Number of training batches 139
Number of test batches 128
| Apache-2.0 | Model/Resnet_18.ipynb | reyvnth/COVIDX |
Data Visualization |
class_names = train_dataset.class_names
def show_images(images, labels, preds):
plt.figure(figsize=(30, 20))
for i, image in enumerate(images):
plt.subplot(1, 25, i + 1, xticks=[], yticks=[])
image = image.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std... | _____no_output_____ | Apache-2.0 | Model/Resnet_18.ipynb | reyvnth/COVIDX |
Creating the Model | resnet18 = torchvision.models.resnet18(pretrained=True)
print(resnet18)
resnet18.fc = torch.nn.Linear(in_features=512, out_features=2)
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(resnet18.parameters(), lr=3e-5)
print(resnet18)
def show_preds():
resnet18.eval()
images, labels = next(iter(... | _____no_output_____ | Apache-2.0 | Model/Resnet_18.ipynb | reyvnth/COVIDX |
Training the Model | def train(epochs):
best_model_wts = copy.deepcopy(resnet18.state_dict())
b_acc = 0.0
t_loss = []
t_acc = []
avg_t_loss=[]
avg_t_acc=[]
v_loss = []
v_acc=[]
avg_v_loss = []
avg_v_acc = []
ep = []
print('Starting training..')
for e in range(0, epochs):
ep.append... | Starting training..
====================
Starting epoch 1/5
====================
Evaluating at step 0
Training Loss: 0.8522, Training Accuracy: 0.4800
| Apache-2.0 | Model/Resnet_18.ipynb | reyvnth/COVIDX |
Final Results VALIDATION LOSS AND TRAINING LOSS VS EPOCHVALIDATION ACCURACY AND TRAINING ACCURACY VS EPOCHBEST ACCURACY ERROR.. | show_preds() | _____no_output_____ | Apache-2.0 | Model/Resnet_18.ipynb | reyvnth/COVIDX |
Plotting Target Pixel Files with Lightkurve Learning GoalsBy the end of this tutorial, you will:- Learn how to download and plot target pixel files from the data archive using [Lightkurve](https://docs.lightkurve.org).- Be able to plot the target pixel file background.- Be able to extract and plot flux from a target ... | import lightkurve as lk
import matplotlib.pyplot as plt
%matplotlib inline | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
1. Downloading a TPF A TPF contains the original imaging data from which a light curve is derived. Besides the brightness data measured by the charge-coupled device (CCD) camera, a TPF also includes post-processing information such as an estimate of the astronomical background, and a recommended pixel aperture for ext... | search_result = lk.search_targetpixelfile("Kepler-8", author="Kepler", quarter=4, cadence="long")
search_result
tpf = search_result.download() | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
This TPF contains data for every cadence in the quarter we downloaded. Let's focus on the first cadence for now, which we can select using zero-based indexing as follows: | first_cadence = tpf[0]
first_cadence | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
2. Flux and Background At each cadence the TPF has a number of photometry data properties. These are:- `flux_bkg`: the astronomical background of the image.- `flux_bkg_err`: the statistical uncertainty on the background flux.- `flux`: the stellar flux after the background is removed.- `flux_err`: the statistical uncer... | first_cadence.flux.value | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
And you can plot the data as follows: | first_cadence.plot(column='flux'); | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
Alternatively, if you are working directly with a FITS file, you can access the data in extension 1 (for example, `first_cadence.hdu[1].data['FLUX']`). Note that you can find all of the details on the structure and contents of TPF files in Section 2.3.2 of the [*Kepler* Archive Manual](http://archive.stsci.edu/files/li... | fig, axes = plt.subplots(2,2, figsize=(16,16))
first_cadence.plot(ax=axes[0,0], column='FLUX')
first_cadence.plot(ax=axes[0,1], column='FLUX_BKG')
first_cadence.plot(ax=axes[1,0], column='FLUX_ERR')
first_cadence.plot(ax=axes[1,1], column='FLUX_BKG_ERR'); | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
From looking at the color scale on both plots, you may see that the background flux is very low compared to the total flux emitted by a star. This is expected — stars are bright! But these small background corrections become important when looking at the very small scale changes caused by planets or stellar oscillation... | first_cadence.plot(bkg=True); | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
In this case, the background is low and the star is bright, so it doesn't appear to make much of a difference. 3. Apertures As part of the data processing done by the *Kepler* pipeline, each TPF includes a recommended *optimal aperture mask*. This aperture mask is optimized to ensure that the stellar signal has a high... | first_cadence.pipeline_mask | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
As you can see, it is a Boolean array detailing which pixels are included. We can plot this aperture over the top of our TPF using the `plot()` function, and passing in the mask to the `aperture_mask` keyword. This will highlight the pixels included in the aperture mask using red hatched lines. | first_cadence.plot(aperture_mask=first_cadence.pipeline_mask); | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
You don't necessarily have to pass in the `pipeline_mask` to the `plot()` function; it can be any mask you create yourself, provided it is the right shape. An accompanying tutorial explains how to create such custom apertures, and goes into aperture photometry in more detail. For specifics on the selection of *Kepler*'... | lc = tpf.to_lightcurve() | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
This method returns a `LightCurve` object which details the flux and flux centroid position at each cadence: | lc | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
Note that this [`KeplerLightCurve`](https://docs.lightkurve.org/api/lightkurve.lightcurve.KeplerLightCurve.html) object has fewer data columns than in light curves downloaded directly from MAST. This is because we are extracting our light curve directly from the TPF using minimal processing, whereas light curves create... | lc.plot(); | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
This light curve is similar to the SAP light curve we previously encountered in the light curve tutorial. NoteThe background flux can be plotted in a similar way, using the [`get_bkg_lightcurve()`](https://docs.lightkurve.org/api/lightkurve.targetpixelfile.KeplerTargetPixelFile.htmllightkurve.targetpixelfile.KeplerTar... | bkg = tpf.get_bkg_lightcurve()
bkg.plot(); | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
Inspecting the background in this way is useful to identify signals which appear to be present in the background rather than in the astronomical object under study. --- Exercises Some stars, such as the planet-hosting star Kepler-10, have been observed both with *Kepler* and *TESS*. In this exercise, download and plot... | #datalist = lk.search_targetpixelfile(...)
#soln:
datalist = lk.search_targetpixelfile("Kepler-10")
datalist
kep = datalist[6].download()
tes = datalist[15].download()
fig, axes = plt.subplots(1, 2, figsize=(14,6))
kep.plot(ax=axes[0], aperture_mask=kep.pipeline_mask, scale='log')
tes.plot(ax=axes[1], aperture_mask=te... | _____no_output_____ | MIT | docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb | alex-w/lightkurve |
Copyright 2018 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](scrollTo=ByZjmtFgB_Y5). | // #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://www.apache.org/licenses/LICE... | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
View on TensorFlow.org Run in Google Colab View source on GitHub Python interoperabilitySwift For TensorFlow supports Python interoperability.You can import Python modules from Swift, call Python functions, and convert values between Swift and Python. | import PythonKit
print(Python.version) | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
Setting the Python version By default, when you `import Python`, Swift searches system library paths for the newest version of Python installed. To use a specific Python installation, set the `PYTHON_LIBRARY` environment variable to the `libpython` shared library provided by the installation. For example: `export PYTH... | // PythonLibrary.useVersion(2)
// PythonLibrary.useVersion(3, 7) | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
__Note: you should run `PythonLibrary.useVersion` right after `import Python`, before calling any Python code. It cannot be used to dynamically switch Python versions.__ Set `PYTHON_LOADER_LOGGING=1` to see [debug output for Python library loading](https://github.com/apple/swift/pull/20674discussion_r235207008). Basi... | // Convert standard Swift types to Python.
let pythonInt: PythonObject = 1
let pythonFloat: PythonObject = 3.0
let pythonString: PythonObject = "Hello Python!"
let pythonRange: PythonObject = PythonObject(5..<10)
let pythonArray: PythonObject = [1, 2, 3, 4]
let pythonDict: PythonObject = ["foo": [0], "bar": [1, 2, 3]]
... | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
`PythonObject` defines conformances to many standard Swift protocols:* `Equatable`* `Comparable`* `Hashable`* `SignedNumeric`* `Strideable`* `MutableCollection`* All of the `ExpressibleBy_Literal` protocolsNote that these conformances are not type-safe: crashes will occur if you attempt to use protocol functionality fr... | let one: PythonObject = 1
print(one == one)
print(one < one)
print(one + one)
let array: PythonObject = [1, 2, 3]
for (i, x) in array.enumerated() {
print(i, x)
} | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
To convert tuples from Python to Swift, you must statically know the arity of the tuple.Call one of the following instance methods:- `PythonObject.tuple2`- `PythonObject.tuple3`- `PythonObject.tuple4` | let pythonTuple = Python.tuple([1, 2, 3])
print(pythonTuple, Python.len(pythonTuple))
// Convert to Swift.
let tuple = pythonTuple.tuple3
print(tuple) | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
Python builtinsAccess Python builtins via the global `Python` interface. | // `Python.builtins` is a dictionary of all Python builtins.
_ = Python.builtins
// Try some Python builtins.
print(Python.type(1))
print(Python.len([1, 2, 3]))
print(Python.sum([1, 2, 3])) | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
Importing Python modulesUse `Python.import` to import a Python module. It works like the `import` keyword in `Python`. | let np = Python.import("numpy")
print(np)
let zeros = np.ones([2, 3])
print(zeros) | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
Use the throwing function `Python.attemptImport` to perform safe importing. | let maybeModule = try? Python.attemptImport("nonexistent_module")
print(maybeModule) | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
Conversion with `numpy.ndarray`The following Swift types can be converted to and from `numpy.ndarray`:- `Array`- `ShapedArray`- `Tensor`Conversion succeeds only if the `dtype` of the `numpy.ndarray` is compatible with the `Element` or `Scalar` generic parameter type.For `Array`, conversion from `numpy` succeeds only i... | import TensorFlow
let numpyArray = np.ones([4], dtype: np.float32)
print("Swift type:", type(of: numpyArray))
print("Python type:", Python.type(numpyArray))
print(numpyArray.shape)
// Examples of converting `numpy.ndarray` to Swift types.
let array: [Float] = Array(numpy: numpyArray)!
let shapedArray = ShapedArray<Flo... | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
Displaying imagesYou can display images in-line using `matplotlib`, just like in Python notebooks. | // This cell is here to display plots inside a Jupyter Notebook.
// Do not copy it into another environment.
%include "EnableIPythonDisplay.swift"
print(IPythonDisplay.shell.enable_matplotlib("inline"))
let np = Python.import("numpy")
let plt = Python.import("matplotlib.pyplot")
let time = np.arange(0, 10, 0.01)
let a... | _____no_output_____ | Apache-2.0 | docs/site/tutorials/python_interoperability.ipynb | texasmichelle/swift |
Example 1: Sandstone Model | # Importing
import theano.tensor as T
import theano
import sys, os
sys.path.append("../GeMpy")
sys.path.append("../")
# Importing GeMpy modules
import gempy as GeMpy
# Reloading (only for development purposes)
import importlib
importlib.reload(GeMpy)
# Usuful packages
import numpy as np
import pandas as pn
import ma... | Function profiling
==================
Message: <ipython-input-6-22dcf15bad61>:3
Time in 5 calls to Function.__call__: 1.357155e+01s
Time in Function.fn.__call__: 1.357096e+01s (99.996%)
Time in thunks: 1.357014e+01s (99.990%)
Total compile time: 2.592983e+01s
Number of Apply nodes: 95
Theano Optimizer... | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
Below here so far is deprecated First we make a GeMpy instance with most of the parameters default (except range that is given by the project). Then we also fix the extension and the resolution of the domain we want to interpolate. Finally we compile the function, only needed once every time we open the project (the g... | # Create a class Grid so far just regular grid
#GeMpy.set_grid(geo_data)
#GeMpy.get_grid(geo_data) | _____no_output_____ | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
Plotting raw data The object Plot is created automatically as we call the methods above. This object contains some methods to plot the data and the results.It is possible to plot a 2D projection of the data in a specific direction using the following method. Also is possible to choose the series you want to plot. Addi... | #GeMpy.plot_data(geo_data, 'y', geo_data.series.columns.values[1]) | _____no_output_____ | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
Class Interpolator This class will take the data from the class Data and calculate potential fields and block. We can pass as key arguments all the variables of the interpolation. I recommend not to touch them if you do not know what are you doing. The default values should be good enough. Also the first time we execu... | %debug
geo_data.interpolator.results
geo_data.interpolator.tg.c_o_T.get_value(), geo_data.interpolator.tg.a_T.get_value()
geo_data.interpolator.compile_potential_field_function()
geo_data.interpolator.compute_potential_fields('BIF_Series',verbose = 3)
geo_data.interpolator.potential_fields
geo_data.interpolator.results... | _____no_output_____ | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
Now we could visualize the individual potential fields as follow: Early granite | GeMpy.plot_potential_field(geo_data,10, n_pf=0) | _____no_output_____ | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
BIF Series | GeMpy.plot_potential_field(geo_data,13, n_pf=1, cmap = "magma", plot_data = True,
verbose = 5) | _____no_output_____ | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
SImple mafic | GeMpy.plot_potential_field(geo_data, 10, n_pf=2) | _____no_output_____ | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
Optimizing the export of lithologiesBut usually the final result we want to get is the final block. The method `compute_block_model` will compute the block model, updating the attribute `block`. This attribute is a theano shared function that can return a 3D array (raveled) using the method `get_value()`. | GeMpy.compute_block_model(geo_data)
#GeMpy.set_interpolator(geo_data, u_grade = 0, compute_potential_field=True) | _____no_output_____ | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
And again after computing the model in the Plot object we can use the method `plot_block_section` to see a 2D section of the model | GeMpy.plot_section(geo_data, 13, direction='y') | _____no_output_____ | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
Export to vtk. (*Under development*) | """Export model to VTK
Export the geology blocks to VTK for visualisation of the entire 3-D model in an
external VTK viewer, e.g. Paraview.
..Note:: Requires pyevtk, available for free on: https://github.com/firedrakeproject/firedrake/tree/master/python/evtk
**Optional keywords**:
- *vtk_filename* = string : fil... | _____no_output_____ | MIT | Prototype Notebook/Example_1_Sandstone.ipynb | nre-aachen/gempy |
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