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 |
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Slice Sessions from the Dataframe | list_sessions = []
list_last_clicked = []
list_last_clicked_temp = []
current_id = df.loc[0, 'user_session']
current_index = 0
columns = ['embedding_'+str(i) for i in range(embeddings.shape[1])]
columns.append('price_standardized')
columns.insert(0, 'product_id')
for i in range(df.shape[0]):
if df.loc[i, 'user_se... | _____no_output_____ | MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Delete Sessions with Length larger than 30 | print(len(list_sessions))
list_sessions_filtered = []
list_last_clicked_filtered = []
list_last_clicked_temp_filtered = []
for index, session in enumerate(list_sessions):
if not (session.shape[0] > 30):
if not (session['product_id'].isin(products_to_delete).any()):
list_sessions_filtered.append... | 61295
| MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Slice Sessions if label and last product from session is the sameExample:- From: session: [ 1506 1506 11410 11410 2826 2826], ground truth: 2826- To: session: [ 1506 1506 11410 11410], ground truth: 2826 | print("Length before", len(list_sessions_filtered))
list_sessions_processed = []
list_last_clicked_processed = []
list_session_processed_autoencoder = []
for i, session in enumerate(list_sessions_filtered):
if session['product_id'].values[-1] == list_last_clicked_filtered[i]:
mask = session['product_id'].v... | Length before 44551
Length after 30941
| MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Create Item IDs starting from value 1 for Embeddings and One Hot Layer | mapping = pd.read_csv('../ID_Mapping.csv')[['Item_ID', 'Mapped_ID']]
dict_items = mapping.set_index('Item_ID').to_dict()['Mapped_ID']
for index, session in enumerate(list_session_processed_autoencoder):
session['product_id'] = session['product_id'].map(dict_items)
# Pad all Sessions with 0. Embedding Layer and LST... | n_output_features 9494
n_unique_input_ids 9494
window_length 31
n_input_features 1
| MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Training: Start here if the preprocessing was already executed | sessions_padded = np.load('list_sessions_padded_autoencoder.npy')
print(sessions_padded.shape)
n_output_features = int(sessions_padded.max())
n_unique_input_ids = int(sessions_padded.max())
window_length = sessions_padded.shape[1]
n_input_features = sessions_padded.shape[2] | (30941, 31, 1)
| MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Grid Search HyperparameterDictionary with different hyperparameters to train on.MLflow will track those in a database. | grid_search_dic = {'hidden_layer_size': [300],
'batch_size': [32],
'embedding_dim': [200],
'window_length': [window_length],
'dropout_fc': [0.0], #0.2
'n_output_features': [n_output_features],
'n_input_feat... | _____no_output_____ | MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
LSTM Autoencoder in functional API- Input: x rows (time steps) of Item IDs in a Session- Output: reconstructed Session | def build_autoencoder(window_length=50,
units_lstm_layer=100,
n_unique_input_ids=0,
embedding_dim=200,
n_input_features=1,
n_output_features=3,
dropout_rate=0.1):
inputs = keras.layer... | _____no_output_____ | MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Convert Numpy Array to tf.data.Dataset for better training performanceThe function will return a zipped tf.data.Dataset with the following Shapes:- x: (batches, window_length)- y: (batches,) | def array_to_tf_data_api(train_data_x, train_data_y, batch_size=64, window_length=50,
validate=False):
"""Applies sliding window on the fly by using the TF Data API.
Args:
train_data_x: Input Data as Numpy Array, Shape (rows, n_features)
batch_size: Batch Size.
window_... | _____no_output_____ | MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Custom TF Callback to log Metrics by MLflow | class MlflowLogging(tf.keras.callbacks.Callback):
def __init__(self, **kwargs):
super().__init__() # handles base args (e.g., dtype)
def on_epoch_end(self, epoch, logs=None):
keys = list(logs.keys())
for key in keys:
mlflow.log_metric(str(key), logs.get(key), step=epoch)
cl... | _____no_output_____ | MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Training | with mlflow.start_run() as parent_run:
for params in grid_search_param:
batch_size = params['batch_size']
window_length = params['window_length']
embedding_dim = params['embedding_dim']
dropout_fc = params['dropout_fc']
hidden_layer_size = params['hidden_layer_size']
... | Epoch 1/20
967/967 [==============================] - 95s 70ms/step - loss: 536.0073 - categorical_accuracy: 0.0037 - categorical_session_accuracy: 0.0000e+00
Epoch 2/20
967/967 [==============================] - 65s 67ms/step - loss: 189.0837 - categorical_accuracy: 0.0097 - categorical_session_accuracy: 4.4663e-05
Ep... | MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Linear regression on Boston house prices | from keras import models
from keras import layers
import numpy as np
import matplotlib.pyplot as plt
# Download the data
from keras.datasets import boston_housing
(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
# Look at the dataset
print train_data.shape # 404 samples, 13 features
... | 2.8874634387446383
| MIT | 06_Linear_Regression_Boston_House_Prices.ipynb | alzaia/keras_projects |
**Data crunch example R script**---author: sweet-richarddate: Jan 30, 2022required packages:* `tidyverse` for data handling* `feather` for efficient loading of data* `xgboost` for predictive modelling* `httr` for the automatic upload. | library(tidyverse)
library(feather) | _____no_output_____ | MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
First, we set some **parameters**.* `is_download` controls whether you want to download data or just read prevously downloaded data* `is_upload` set this to TRUE for automatic upload.* `nrounds` is a parameter for `xgboost` that we set to 100 for illustration. You might want to adjust the paramters of xgboost. | #' ## Parameters
file_name_train = "train_data.feather"
file_name_test ="test_data.feather"
is_download = TRUE # set this to true to download new data or to FALSE to load data in feather format
is_upload = FALSE # set this to true to upload a submission
nrounds = 300 # you might want to adjust this one and other parame... | _____no_output_____ | MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
In the **functions** section we defined the correlation measure that we use to measure performance. | #' ## Functions
#+
getCorrMeasure = function(actual, predicted) {
cor_measure = cor(actual, predicted, method="spearman")
return(cor_measure)
} | _____no_output_____ | MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
Now, we either **download** the current data from the servers or load them in feather format. Furthermore, we define the features that we actually want to use. In this illustration we use all of them but `id` and `Moons`. | #' ## Download data
#' after the download, data is stored in feather format to be read on demand quickly. Data is stored in integer format to save memory.
#+
if( is_download ) {
cat("\n start download")
train_datalink_X = 'https://tournament.datacrunch.com/data/X_train.csv'
train_datalink_y = 'https://tour... |
start download | MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
Next we fit our go-to algorithm **xgboost** with mainly default parameters, only `eta` and `max_depth` are set. | #' ## Fit xgboost
#+ cache = TRUE
library(xgboost, warn.conflicts = FALSE)
# custom loss function for eval
corrmeasure <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label")
corrm <- as.numeric(cor(labels, preds, method="spearman"))
return(list(metric = "corr", value = corrm))
}
eval_metric_string... |
starting xgboost
| MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
**First target** `target_r` | # first target target_r then g and b
################
current_target = "target_r"
dtrain = xgb.DMatrix(train_data %>% select(one_of(model_vars)) %>% as.matrix(), label = train_data %>% select(one_of(current_target)) %>% as.matrix())
xgb.model.tree = xgb.train(data = dtrain,
params = ... |
: metric: rmse
[1] "Corrm on train: 0.0867"
[1] "xgboost target_r ready"
| MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
**Second target** `target_g` | # second target target_g
################
current_target = "target_g"
dtrain = xgb.DMatrix(train_data %>% select(one_of(model_vars)) %>% as.matrix(), label = train_data %>% select(one_of(current_target)) %>% as.matrix())
xgb.model.tree = xgb.train(data = dtrain,
params = tree.params, n... |
: metric: rmse
[1] "Corrm on train: 0.1099"
[1] "xgboost target_g ready"
| MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
**Third target** `target_b` | # third target target_b
################
current_target = "target_b"
dtrain = xgb.DMatrix(train_data %>% select(one_of(model_vars)) %>% as.matrix(), label = train_data %>% select(one_of(current_target)) %>% as.matrix())
xgb.model.tree = xgb.train(data = dtrain,
params = tree.params, nrou... |
: metric: rmse
[1] "Corrm on train: 0.1232"
[1] "xgboost target_b ready"
| MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
Then we produce simply histogram plots to see whether the predictions are plausible and prepare a **submission file**: | #' ## Submission
#' simple histograms to check the submissions
#+
hist(xgboost_tree_live_pred1)
hist(xgboost_tree_live_pred2)
hist(xgboost_tree_live_pred3)
#' create submission file
#+
sub_df = tibble(target_r = xgboost_tree_live_pred1,
target_g = xgboost_tree_live_pred2,
target_b =... | _____no_output_____ | MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
Finally, we can **automatically upload** the file to the server: | #' ## Upload submission
#+
if( is_upload ) {
library(httr)
API_KEY = "YourKeyHere"
response <- POST(
url = "https://tournament.crunchdao.com/api/v2/submissions",
query = list(apiKey = API_KEY),
body = list(
file = upload_file(path = paste0("./", file_name_submission))
),
encode = c... | _____no_output_____ | MIT | dcrunch_R_example.ipynb | rwarnung/datacrunch-notebooks |
View source on GitHub Notebook Viewer Run in binder Run in Google Colab Install Earth Engine APIInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geehydro](https://github.com/giswqs/geehydro). The **geehydro** Python package builds on the [folium](http... | # %%capture
# !pip install earthengine-api
# !pip install geehydro | _____no_output_____ | MIT | Datasets/Terrain/srtm_mtpi.ipynb | dmendelo/earthengine-py-notebooks |
Import libraries | import ee
import folium
import geehydro | _____no_output_____ | MIT | Datasets/Terrain/srtm_mtpi.ipynb | dmendelo/earthengine-py-notebooks |
Authenticate and initialize Earth Engine API. You only need to authenticate the Earth Engine API once. Uncomment the line `ee.Authenticate()` if you are running this notebook for the first time or if you are getting an authentication error. | # ee.Authenticate()
ee.Initialize() | _____no_output_____ | MIT | Datasets/Terrain/srtm_mtpi.ipynb | dmendelo/earthengine-py-notebooks |
Create an interactive map This step creates an interactive map using [folium](https://github.com/python-visualization/folium). The default basemap is the OpenStreetMap. Additional basemaps can be added using the `Map.setOptions()` function. The optional basemaps can be `ROADMAP`, `SATELLITE`, `HYBRID`, `TERRAIN`, or `... | Map = folium.Map(location=[40, -100], zoom_start=4)
Map.setOptions('HYBRID') | _____no_output_____ | MIT | Datasets/Terrain/srtm_mtpi.ipynb | dmendelo/earthengine-py-notebooks |
Add Earth Engine Python script | dataset = ee.Image('CSP/ERGo/1_0/Global/SRTM_mTPI')
srtmMtpi = dataset.select('elevation')
srtmMtpiVis = {
'min': -200.0,
'max': 200.0,
'palette': ['0b1eff', '4be450', 'fffca4', 'ffa011', 'ff0000'],
}
Map.setCenter(-105.8636, 40.3439, 11)
Map.addLayer(srtmMtpi, srtmMtpiVis, 'SRTM mTPI')
| _____no_output_____ | MIT | Datasets/Terrain/srtm_mtpi.ipynb | dmendelo/earthengine-py-notebooks |
Display Earth Engine data layers | Map.setControlVisibility(layerControl=True, fullscreenControl=True, latLngPopup=True)
Map | _____no_output_____ | MIT | Datasets/Terrain/srtm_mtpi.ipynb | dmendelo/earthengine-py-notebooks |
Introduction to Data ScienceSee [Lesson 1](https://www.udacity.com/course/intro-to-data-analysis--ud170)You should run it in local Jupyter env as this notebook refers to local dataset | import unicodecsv
from datetime import datetime as dt
enrollments_filename = 'dataset/enrollments.csv'
engagement_filename = 'dataset/daily_engagement.csv'
submissions_filename = 'dataset/project_submissions.csv'
## Longer version of code (replaced with shorter, equivalent version below)
def read_csv(filename):
... | _____no_output_____ | MIT | learning/ud170/lesson-1.ipynb | WL152/project-omega |
#@title Students Grade in OOP
Student_Name1= "Enter the student name"#@param{type: "string"}
prelim= 90#@param{type: "number"}
midterm= 95#@param{type: "number"}
final= 100#@param{type: "number"}
semestral_grade=(prelim+midterm+final)/3
print("The prelim grade of student 1 is"+" "+ str(prelim))
print("The midterm gra... | The prelim grade of student 1 is 90
The midterm grade of student 1 is 95
The final grade of student 1 is 100
The semestral grade of student1 is 95.0
Female
| Apache-2.0 | GUI_Application.ipynb | SarahRebulado/OOP1_2 | |
This notebook contains code for model comparison. Optimal hyperparameters for models are supposed to be already found. Imports | #imports
!pip install scipydirect
import math
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn import preprocessing
from sklearn.preprocessing import normalize
from sklearn.ensemble import AdaBoostClassifier
f... | Collecting scipydirect
[?25l Downloading https://files.pythonhosted.org/packages/c2/dd/657e6c53838b3ff50e50bda4e905c8ec7e4b715f966f33d0566088391d75/scipydirect-1.3.tar.gz (49kB)
[K |██████▋ | 10kB 15.9MB/s eta 0:00:01
[K |█████████████▏ | 20kB 21.2MB/s eta 0:00:01
... | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
1) Describe classes of the following models: AdaFair, SMOTEBoost, ASR 1.1) AdaFair | #AdaFair
class AdaFairClassifier(AdaBoostClassifier):
def __init__(self,
base_estimator=None, *,
n_estimators=50,
learning_rate=1,
algorithm='SAMME',
random_state=42,
protected=None,
epsilon = 0):
... | _____no_output_____ | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
1.2 SMOTEBoost | #SMOTEBoost
random_state = 42
#4, y - true label
def ada_boost_eps(y, y_pred_t, distribution):
eps = np.sum((1 - (y == y_pred_t) + (np.logical_not(y) == y_pred_t)) * distribution)
return eps
#5
def ada_boost_betta(eps):
betta = eps/(1 - eps)
return betta
def ada_boost_w(y, y_pred_t):
w = 0.5 * (1 + (y == ... | _____no_output_____ | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
1.3 Adaptive sensitive reweighting | #Adaptive sensitive reweighting
class ReweightedClassifier:
def __init__(self, baze_clf, alpha, beta, params = {}):
"""
Input:
baze_clf - object from sklearn with methods .fit(sample_weight=), .predict(), .predict_proba()
alpha - list of alphas for sensitive and non-sensitive... | _____no_output_____ | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
1.4 Some functions used for fitting models, calculating metrics, and data separation | #This function returns binary list of whether the corresponding feature is protected (1) or not (0)
def get_protected_instances(X, feature, label):
protected = []
for i in range(len(X)):
if X.iloc[i][feature] == label:
protected.append(1)
else: protected.append(0)
return protected
#To calculate TRP... | _____no_output_____ | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
2) Download datasets (run one cell for one dataset) Adult census | #Adult census
#adult_census_names = ['old_id' ,'age','workclass','fnlwgt','education','education_num','marital_status','occupation','relationship','race','sex','capital_gain','capital_loss','hours_per_week','native_country']
X_train = pd.read_csv("splits/X_train_preprocessed_adult.csv").drop("Unnamed: 0", axis = 1)#, n... | _____no_output_____ | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
Bank | X_train = pd.read_csv("splits/X_train_preprocessed_bank.csv").drop("Unnamed: 0", axis = 1)#, names = adult_census_names).iloc[1:]
X_test = pd.read_csv("splits/X_test_preprocessed_bank.csv").drop("Unnamed: 0", axis = 1)#, names = adult_census_names).iloc[1:]
y_train = pd.read_csv("splits/y_train_preprocessed_bank.csv")... | _____no_output_____ | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
Compass | X_train = pd.read_csv("splits/X_train_preprocessed_compas.csv").drop("Unnamed: 0", axis = 1)#, names = adult_census_names).iloc[1:]
X_test = pd.read_csv("splits/X_test_preprocessed_compas.csv").drop("Unnamed: 0", axis = 1)#, names = adult_census_names).iloc[1:]
y_train = pd.read_csv("splits/y_train_preprocessed_compas... | _____no_output_____ | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
KDD Census | #adult_census_names = ['old_id' ,'age','workclass','fnlwgt','education','education_num','marital_status','occupation','relationship','race','sex','capital_gain','capital_loss','hours_per_week','native_country']
X_train = pd.read_csv("splits/X_train_preprocessed_kdd.csv").drop("Unnamed: 0", axis = 1)#, names = adult_cen... | 98147 98147
| MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
3) Create models, train classifiers | #Regression
# Create model with obtained hyperparameters alpha, alpha', beta, beta'
model_reweighted_classifier = ReweightedClassifier(LogisticRegression, [a_1[0], a_1[1]], [a_1[2], a_1[3]], params = {"max_iter": 4})
# Train model on X_train
model_reweighted_classifier.fit(X_train, y_train, X_test, y_test, minority_i... | _____no_output_____ | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
4) Compute and plot metrics 4.1 Compute | names = ['ada_fair','ada_boost_sklearn', 'smoteboost', "reweighted_classifier"]
classifiers = [ada_fair, ada_boost_sklearn, smoteboost1, model_reweighted_classifier]
accuracy = {}
bal_accuracy = {}
TPR = {}
TNR = {}
eq_odds = {}
p_rule = {}
#DELETA
#y_test = y_test[:][1]
for i, clf in enumerate(classifiers):
prin... | ada_fair
accuracy ada_fair: 0.9228809846454807
balanced accuracy ada_fair: 0.7176952582693732
TPR protected ada_fair: 0.6626686656671664
TNR protected ada_fair: 0.9330514588008977
TPR non protected ada_fair: 0.4335117332235488
TNR non protected ada_fair: 0.9756010558607405
pRule ada_fair: 0.8097128558491885
ada_boost_s... | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
4.2 Plot | labels = ['Accuracy', 'Bal. accuracy', 'Eq. odds','TPR prot.', 'TPR non-prot', 'TNR prot.', 'TNR non-prot.', 'pRule']
adaFair_metrics = [accuracy['ada_fair'], bal_accuracy['ada_fair'], eq_odds['ada_fair'], TPR['ada_fair protected'], TPR['ada_fair non protected'], TNR['ada_fair protected'], TNR['ada_fair non protected']... | _____no_output_____ | MIT | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification |
Apple and Tesla Split on 8/31 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math
import warnings
warnings.filterwarnings("ignore")
# for fetching data
import yfinance as yf
# input
# Coronavirus 2nd Wave
title = "Apple and Tesla"
symbols = ['AAPL', 'TSLA']
start = '2020-01-01'
end = '2020-08-3... | _____no_output_____ | MIT | Python_Stock/Portfolio_Strategies/Apple_Tesla_Split.ipynb | linusqzdeng/Stock_Analysis_For_Quant |
- A stationary time series is one whose statistical properties such as mean, variance, autocorrelation, etc. are all constant over time. Most statistical forecasting methods are based on the assumption that the time series can be rendered approximately stationary (i.e., "stationarized") through the use of mathematical ... | from statsmodels.tsa.stattools import adfuller
def test_stationary(timeseries):
#Determing rolling statistics
moving_average=timeseries.rolling(window=12).mean()
standard_deviation=timeseries.rolling(window=12).std()
#Plot rolling statistics:
plt.plot(timeseries,color='blue',label="Origina... | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
- There are 2 major reasons behind non-stationaruty of a TS:- - Trend – varying mean over time. For eg, in this case we saw that on average, the number of passengers was growing over time.- - Seasonality – variations at specific time-frames. eg people might have a tendency to buy cars in a particular month because of p... | indexedDataset_logscale=np.log(indexedDataset)
test_stationary(indexedDataset_logscale) | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
Dataset Log Minus Moving Average (dl_ma) | rolmeanlog=indexedDataset_logscale.rolling(window=12).mean()
dl_ma=indexedDataset_logscale-rolmeanlog
dl_ma.head(12)
dl_ma.dropna(inplace=True)
dl_ma.head(12)
test_stationary(dl_ma) | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
Exponential Decay Weighted Average (edwa) | edwa=indexedDataset_logscale.ewm(halflife=12,min_periods=0,adjust=True).mean()
plt.plot(indexedDataset_logscale)
plt.plot(edwa,color='red') | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
Dataset Logscale Minus Moving Exponential Decay Average (dlmeda) | dlmeda=indexedDataset_logscale-edwa
test_stationary(dlmeda) | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
Eliminating Trend and Seasonality - Differencing – taking the differece with a particular time lag- Decomposition – modeling both trend and seasonality and removing them from the model. Differencing Dataset Log Div Shifting (dlds) | #Before Shifting
indexedDataset_logscale.head()
#After Shifting
indexedDataset_logscale.shift().head()
dlds=indexedDataset_logscale-indexedDataset_logscale.shift()
dlds.dropna(inplace=True)
test_stationary(dlds) | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
Decomposition | from statsmodels.tsa.seasonal import seasonal_decompose
decompostion= seasonal_decompose(indexedDataset_logscale,freq=10)
trend=decompostion.trend
seasonal=decompostion.seasonal
residual=decompostion.resid
plt.subplot(411)
plt.plot(indexedDataset_logscale,label='Original')
plt.legend(loc='best')
plt.subplot(412)
plt... | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
- Here trend, seasonality are separated out from data and we can model the residuals. Lets check stationarity of residuals: | decomposedlogdata=residual
decomposedlogdata.dropna(inplace=True)
test_stationary(decomposedlogdata) | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
Forecasting a Time Series - ARIMA stands for Auto-Regressive Integrated Moving Averages. The ARIMA forecasting for a stationary time series is nothing but a linear (like a linear regression) equation. The predictors depend on the parameters (p,d,q) of the ARIMA model:- - Number of AR (Auto-Regressive) terms (p): AR te... | from statsmodels.tsa.stattools import acf,pacf
lag_acf=acf(dlds,nlags=20)
lag_pacf=pacf(dlds,nlags=20,method='ols')
plt.subplot(121)
plt.plot(lag_acf)
plt.axhline(y=0, linestyle='--',color='gray')
plt.axhline(y=1.96/np.sqrt(len(dlds)),linestyle='--',color='gray')
plt.axhline(y=-1.96/np.sqrt(len(dlds)),linestyle='--',c... | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
- In this plot, the two dotted lines on either sides of 0 are the confidence interevals. These can be used to determine the ‘p’ and ‘q’ values as:- - p – The lag value where the PACF chart crosses the upper confidence interval for the first time. If we notice closely, in this case p=2.- - q – The lag value where the AC... | from statsmodels.tsa.arima_model import ARIMA
model=ARIMA(indexedDataset_logscale,order=(5,1,0))
results_AR=model.fit(disp=-1)
plt.plot(dlds)
plt.plot(results_AR.fittedvalues,color='red')
plt.title('RSS: %.4f'%sum((results_AR.fittedvalues-dlds['MONSOON'])**2))
print('Plotting AR Model')
model = ARIMA(indexedDataset_lo... | Plotting Combined Model
| MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
Taking it back to original scale from residual scale | #storing the predicted results as a separate series
predictions_ARIMA_diff = pd.Series(results_ARIMA.fittedvalues, copy=True)
predictions_ARIMA_diff.head() | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
- Notice that these start from ‘1949-02-01’ and not the first month. Why? This is because we took a lag by 1 and first element doesn’t have anything before it to subtract from. The way to convert the differencing to log scale is to add these differences consecutively to the base number. An easy way to do it is to first... | #convert to cummuative sum
predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()
predictions_ARIMA_diff_cumsum
predictions_ARIMA_log = pd.Series(indexedDataset_logscale['MONSOON'].ix[0], index=indexedDataset_logscale.index)
predictions_ARIMA_log
predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_AR... | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
- Here the first element is base number itself and from there on the values cumulatively added. | #Last step is to take the exponent and compare with the original series.
predictions_ARIMA = np.exp(predictions_ARIMA_log)
plt.plot(indexedDataset)
plt.plot(predictions_ARIMA)
plt.title('RMSE: %.4f'% np.sqrt(sum((predictions_ARIMA-indexedDataset['MONSOON'])**2)/len(indexedDataset))) | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
- Finally we have a forecast at the original scale. | results_ARIMA.plot_predict(1,26)
#start = !st month
#end = 10yrs forcasting = 144+12*10 = 264th month
#Two models corresponds to AR & MA
x=results_ARIMA.forecast(steps=5)
print(x)
#values in residual equivalent
for i in range(0,5):
print(x[0][i],end='')
print('\t',x[1][i],end='')
print('\t',x[2][i])
np.ex... | _____no_output_____ | MIT | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 |
1. Create an assert statement that throws an AssertionError if the variable spam is a negative integer. | import pyinputplus as pyip
def is_pos_integer():
n = pyip.inputInt(prompt='Enter a positive integer: ')
assert n > 0, 'This is a negative integer'
is_pos_integer() | Enter a positive integer: -1
| MIT | Python_Basic_Assignments/Assignment_11.ipynb | dataqueenpend/-Assignments_fsDS_OneNeuron |
2. Write an assert statement that triggers an AssertionError if the variables eggs and bacon contain strings that are the same as each other, even if their cases are different (that is, &39;hello&39; and &39;hello&39; are considered the same, and &39;goodbye&39; and &39;GOODbye&39; are also considered the same). | import re
import pyinputplus as pyip
def same_or_different():
eggs = pyip.inputStr(prompt='What is eggs: ')
bacon = pyip.inputStr(prompt='What is bacon: ')
assert eggs.lower() != bacon.lower(), 'Strings are the same!'
same_or_different() | What is eggs: hello
What is bacon: Hello
| MIT | Python_Basic_Assignments/Assignment_11.ipynb | dataqueenpend/-Assignments_fsDS_OneNeuron |
3. Create an assert statement that throws an AssertionError every time. | assert 0 !=0 , 'Assertion Error every time!' | _____no_output_____ | MIT | Python_Basic_Assignments/Assignment_11.ipynb | dataqueenpend/-Assignments_fsDS_OneNeuron |
2 Dead reckoning*Dead reckoning* is a means of navigation that does not rely on external observations. Instead, a robot’s position is estimated by summing its incremental movements relative to a known starting point.Estimates of the distance traversed are usually obtained from measuring how many times the wheels have ... | from nbev3devsim.load_nbev3devwidget import roboSim, eds
%load_ext nbev3devsim | _____no_output_____ | OML | content/04. Not quite intelligent robots/04.2 Robot navigation using dead reckoning.ipynb | mmh352/tm129-robotics2020 |
To navigate the environment, we will use a small robot configuration within the simulator. The robot configuration can be set via the simulator user interface, or by passing the `-r Small_Robot` parameter setting in the simulator magic.The following program should drive the robot from its starting point to the target, ... | %%sim_magic_preloaded -b FLL_2018_Into_Orbit -p -r Small_Robot
# Turn on the spot to the right
tank_turn.on_for_rotations(100, SpeedPercent(70), 1.7 )
# Go forwards
tank_drive.on_for_rotations(SpeedPercent(30), SpeedPercent(30), 20)
# Slight graceful turn to left
tank_drive.on_for_rotations(SpeedPercent(35), SpeedPe... | _____no_output_____ | OML | content/04. Not quite intelligent robots/04.2 Robot navigation using dead reckoning.ipynb | mmh352/tm129-robotics2020 |
*Add your notes on how well the simulated robot performed the task here.* To set the speeds and times, I used a bit of trial and error.If the route had been much more complex, then I would have been tempted to comment out the steps up I had already run and add new steps that would be applied from wherever the robot was... | # YOUR CODE HERE | _____no_output_____ | OML | content/04. Not quite intelligent robots/04.2 Robot navigation using dead reckoning.ipynb | mmh352/tm129-robotics2020 |
*Your notes and observations here.* 2.2 Challenge – Reaching the moon base In the following code cell, write a program to move the simulated robot from its location servicing the satellite to the moon base identified as the circular area marked on the moon in the top right-hand corner of the simulated world.In the sim... | %%sim_magic_preloaded
# YOUR CODE HERE
| _____no_output_____ | OML | content/04. Not quite intelligent robots/04.2 Robot navigation using dead reckoning.ipynb | mmh352/tm129-robotics2020 |
2.3 Dead reckoning with noiseThe robot traverses its path using timing information for dead reckoning. In principle, if the simulated robot had a map then it could calculate all the distances and directions for itself, convert these to times, and dead reckon its way to the target. However, there is a problem with dead... | %sim_magic -b Empty_Map --clear | _____no_output_____ | OML | content/04. Not quite intelligent robots/04.2 Robot navigation using dead reckoning.ipynb | mmh352/tm129-robotics2020 |
Run the following code cell to download the program to the simulator using an empty background (select the *Empty_Map*) and the *Pen Down* mode selected. Also reset the initial location of the robot to an *x* value of `150` and *y* value of `400`.Run the program in the simulator and observe what happens. | %%sim_magic_preloaded -b Empty_Map -p -x 150 -y 400 -r Small_Robot --noisecontrols
tank_drive.on_for_rotations(SpeedPercent(30),
SpeedPercent(30), 10) | _____no_output_____ | OML | content/04. Not quite intelligent robots/04.2 Robot navigation using dead reckoning.ipynb | mmh352/tm129-robotics2020 |
*Record your observations here describing what happens when you run the program.* When you run the program, you should see the robot drive forwards a short way in a straight line, leaving a straight line trail behind it.Reset the location of the robot. Within the simulator, use the *Noise controls* to increase the *Whe... | %sim_magic -C | _____no_output_____ | OML | content/04. Not quite intelligent robots/04.2 Robot navigation using dead reckoning.ipynb | mmh352/tm129-robotics2020 |
Now run the original satellite-finding dead-reckoning program again, using the *FLL_2018_Into_Orbit* background, but in the presence of *Wheel noise*. How well does it perform this time compared to previously? | %%sim_magic_preloaded -b FLL_2018_Into_Orbit -p -r Small_Robot
# Turn on the spot to the right
tank_turn.on_for_rotations(100, SpeedPercent(70), 1.7 )
# Go forwards
tank_drive.on_for_rotations(SpeedPercent(30), SpeedPercent(30), 20)
# Slight graceful turn to left
tank_drive.on_for_rotations(SpeedPercent(35), SpeedPe... | _____no_output_____ | OML | content/04. Not quite intelligent robots/04.2 Robot navigation using dead reckoning.ipynb | mmh352/tm129-robotics2020 |
What this code doesIn short, it is a reverse meme search, that identifies the source of the meme. It takes an image copypasta, extracts the individual *subimages* and compares it with a database of pictures (the database should be made up of copypastas, which is in TODO) TODO Clean up the codeThere are many repetitive... | %run image_database_helper.ipynb
model = init_model() | _____no_output_____ | MIT | database_updater.ipynb | tlkh/reverse-image-search |
making a list of all the files | !rm 'imgs/.DS_Store'
images = findfiles("new/")
print(len(images)) | 24
| MIT | database_updater.ipynb | tlkh/reverse-image-search |
Processing pictures | from PIL import Image
from matplotlib.pyplot import imshow
import matplotlib.pyplot as plt
import cv2
import csv
fieldnames = ['img_file_name',
'number_of_subimages',
'subimage_number',
'x',
'y',
'w',
'h',
'feature_vector... | _____no_output_____ | MIT | database_updater.ipynb | tlkh/reverse-image-search |
Computer Vision Nanodegree Project: Image Captioning---In this notebook, you will use your trained model to generate captions for images in the test dataset.This notebook **will be graded**. Feel free to use the links below to navigate the notebook:- [Step 1](step1): Get Data Loader for Test Dataset - [Step 2](step2)... | import sys
sys.path.append('/opt/cocoapi/PythonAPI')
from pycocotools.coco import COCO
from data_loader import get_loader
from torchvision import transforms
# TODO #1: Define a transform to pre-process the testing images.
transform_test = transforms.Compose([
transforms.Resize(256), # sma... | Vocabulary successfully loaded from vocab.pkl file!
| MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
Run the code cell below to visualize an example test image, before pre-processing is applied. | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Obtain sample image before and after pre-processing.
orig_image, image = next(iter(data_loader))
# Visualize sample image, before pre-processing.
plt.imshow(np.squeeze(orig_image))
plt.title('example image')
plt.show() | _____no_output_____ | MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
Step 2: Load Trained ModelsIn the next code cell we define a `device` that you will use move PyTorch tensors to GPU (if CUDA is available). Run this code cell before continuing. | import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | _____no_output_____ | MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
Before running the code cell below, complete the following tasks. Task 1In the next code cell, you will load the trained encoder and decoder from the previous notebook (**2_Training.ipynb**). To accomplish this, you must specify the names of the saved encoder and decoder files in the `models/` folder (e.g., these name... | # Watch for any changes in model.py, and re-load it automatically.
% load_ext autoreload
% autoreload 2
import os
import torch
from model import EncoderCNN, DecoderRNN
# TODO #2: Specify the saved models to load.
encoder_file = "encoder-1.pkl"
decoder_file = "decoder-1.pkl"
# TODO #3: Select appropriate values for ... | Downloading: "https://download.pytorch.org/models/resnet50-19c8e357.pth" to /root/.torch/models/resnet50-19c8e357.pth
100%|██████████| 102502400/102502400 [00:01<00:00, 58944421.27it/s]
| MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
Step 3: Finish the SamplerBefore executing the next code cell, you must write the `sample` method in the `DecoderRNN` class in **model.py**. This method should accept as input a PyTorch tensor `features` containing the embedded input features corresponding to a single image.It should return as output a Python list `o... | # Move image Pytorch Tensor to GPU if CUDA is available.
image = image.to(device)
# Obtain the embedded image features.
features = encoder(image).unsqueeze(1)
# Pass the embedded image features through the model to get a predicted caption.
output = decoder.sample(features)
print('example output:', output)
assert (ty... | example output: [0, 3, 2436, 170, 77, 3, 204, 21, 3, 769, 77, 32, 297, 18, 1, 1, 18, 1, 1, 18]
| MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
Step 4: Clean up the CaptionsIn the code cell below, complete the `clean_sentence` function. It should take a list of integers (corresponding to the variable `output` in **Step 3**) as input and return the corresponding predicted sentence (as a single Python string). | # TODO #4: Complete the function.
def clean_sentence(output):
seperator = " "
word_list = [];
for word_index in output:
if word_index not in [0,2]: # 0: '<start>', 1: '<end>', 2: '<unk>', 18: '.'
if word_index == 1:
break
word = data_loader.dataset.vocab.... | _____no_output_____ | MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
After completing the `clean_sentence` function above, run the code cell below. If the cell returns an assertion error, then please follow the instructions to modify your code before proceeding. | sentence = clean_sentence(output)
print('example sentence:', sentence)
assert type(sentence)==str, 'Sentence needs to be a Python string!' | example sentence: a giraffe standing in a field with a tree in the background .
| MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
Step 5: Generate Predictions!In the code cell below, we have written a function (`get_prediction`) that you can use to use to loop over images in the test dataset and print your model's predicted caption. | def get_prediction():
orig_image, image = next(iter(data_loader))
plt.imshow(np.squeeze(orig_image))
plt.title('Sample Image')
plt.show()
image = image.to(device)
features = encoder(image).unsqueeze(1)
output = decoder.sample(features)
sentence = clean_sentence(output)
print(sent... | _____no_output_____ | MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
Run the code cell below (multiple times, if you like!) to test how this function works. | get_prediction() | _____no_output_____ | MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
As the last task in this project, you will loop over the images until you find four image-caption pairs of interest:- Two should include image-caption pairs that show instances when the model performed well.- Two should highlight image-caption pairs that highlight instances where the model did not perform well.Use the ... | get_prediction()
get_prediction() | _____no_output_____ | MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
The model could have performed better ...Use the next two code cells to loop over captions. Save the notebook when you encounter two images with relatively inaccurate captions. | get_prediction()
get_prediction() | _____no_output_____ | MIT | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning |
Purpose: To run the full segmentation using the best scored method from 2_compare_auto_to_manual_threshold Date Created: January 7, 2022 Dates Edited: January 26, 2022 - changed the ogd severity study to be the otsu data as the yen data did not run on all samples. *Step 1: Import Necessary Packages* | # import major packages
import numpy as np
import matplotlib.pyplot as plt
import skimage
import PIL as Image
import os
import pandas as pd
# import specific package functions
from skimage.filters import threshold_otsu
from skimage import morphology
from scipy import ndimage
from skimage.measure import label
from skim... | _____no_output_____ | MIT | 1_microglia_segmentation/OGD_3_full_segmentation_pipeline-Copy1.ipynb | Nance-Lab/microFIBER |
__OGD Severity Study__ | im_folder_location = '/Users/hhelmbre/Desktop/ogd_severity_undergrad/10_4_21_redownload/'
im_paths = []
files = []
for file in os.listdir(im_folder_location):
if file.endswith(".tif"):
file_name = os.path.join(im_folder_location, file)
files.append(file)
im_paths.append(file_name)
files
prop... | Python implementation: CPython
Python version : 3.7.4
IPython version : 7.8.0
numpy : 1.21.5
pandas : 1.3.5
scipy : 1.3.1
skimage : 0.17.2
matplotlib: 3.1.1
wget : 3.2
Compiler : Clang 4.0.1 (tags/RELEASE_401/final)
OS : Darwin
Release : 20.6.0
Machine : x86_64
Process... | MIT | 1_microglia_segmentation/OGD_3_full_segmentation_pipeline-Copy1.ipynb | Nance-Lab/microFIBER |
Basic Workflow | # Always have your imports at the top
import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier
from sklearn.base import TransformerMixin
from hashlib import sha1 # just for grading purposes
import json # just for grading... | _____no_output_____ | MIT | stats-279/SLU19 - Workflow/Exercise notebook.ipynb | hershaw/stats-279 |
Workflow stepsWhat are the basic workflow steps?It's incredibly obvious what the steps are since you can see them graded in plain text. However we deem it worth actually making you type each one of the steps and take a moment to think about it and internalize them.Please do actually type them rather than just copy-pas... | # step_1 = ...
# step_2 = ...
# step_2_a = ...
# step_2_b = ...
# step_2_c = ...
# step_2_d = ...
# step_3 = ...
# step_4 = ...
# step_5 = ...
# YOUR CODE HERE
raise NotImplementedError()
### BEGIN TESTS
assert step_1 == 'Get the data'
assert step_2 == 'Data analysis and preparation'
assert step_2_a == 'Data analysis... | _____no_output_____ | MIT | stats-279/SLU19 - Workflow/Exercise notebook.ipynb | hershaw/stats-279 |
Specific workflow questionsHere are some more specific questions about individual workflow steps. | # True or False, it's super easy to gather your dataset in a production environment
# real_world_dataset_gathering_easy = ...
# True or False, it's super easy to gather your dataset in the context of the academy
# academy_dataset_gathering_easy = ...
# True or False, you should try as hard as you can to get the best ... | _____no_output_____ | MIT | stats-279/SLU19 - Workflow/Exercise notebook.ipynb | hershaw/stats-279 |
scikit pipelinesMake a simple pipeline that1. Drops all columns that start with the string `evil`1. Fills all nulls with the median | # Create a pipeline step called RemoveEvilColumns the removed any
# column whose name starts with the string 'evil'
# YOUR CODE HERE
raise NotImplementedError()
# Create an pipeline using make_pipeline
# 1. removes evil columns
# 2. imputes with the mean
# 3. has a random forest classifier as the last step
# YOUR C... | _____no_output_____ | MIT | stats-279/SLU19 - Workflow/Exercise notebook.ipynb | hershaw/stats-279 |
import pandas as pd
path="https://raw.githubusercontent.com/Sarbajit097/Assignment/main/Toyota.csv"
data =pd.read_csv(path)
data
type(data)
data.shape
data.info()
data.index
data.columns
data.head()
data.tail()
data.head(5)
data[['Price',"Age"]].head(10)
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum(... | _____no_output_____ | Apache-2.0 | Assignment_3.ipynb | Sarbajit097/Assignment | |
For Loop | week = ["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"]
for x in week:
print(x) | Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
The Break Statement | week = ["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"]
for x in week:
print(x)
if x=="Thursday":
break
week = ["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"]
for x in week:
if x=="Thursday":
break
print(x) | Sunday
Monday
Tuesday
Wednesday
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
Looping through string | for x in "Python Programming":
print(x) | P
y
t
h
o
n
P
r
o
g
r
a
m
m
i
n
g
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
The range () function | for x in range(16):
print(x)
for x in range(2,16):
print(x) | 2
3
4
5
6
7
8
9
10
11
12
13
14
15
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
Nested Loops | adjective=["red","big","tasty"]
fruits = ["apple","banana","cherry"]
for x in adjective:
for y in fruits:
print(x,y) | red apple
red banana
red cherry
big apple
big banana
big cherry
tasty apple
tasty banana
tasty cherry
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
While Loop | i=1
while i<=6:
print(i)
i+=1 #Assignment operator for addition | 1
2
3
4
5
6
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
The break statement | i=1
while i<6:
print(i)
if i==3:
break
i+=1 | 1
2
3
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
The continue statement | i = 0
while i<6:
i+=1 #Assignment operator for addition
if i==3:
continue
print(i) | 1
2
4
5
6
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
The else statement | i = 1
while i<=6:
print(i)
i+=1
else:
print("i is no longer less than 6") | 1
2
3
4
5
6
i is no longer less than 6
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
Application 1 | #Create a Python program that displays Hello 0 to Hello 10 in vertical sequence
hello=["Hello"]
num=["0","1","2","3","4","5","6","7","8","9","10"]
#for loop
for x in hello:
for y in num:
print(x,y)
#while loop
i=0
while i<=10:
print("Hello",i)
i+=1 #Assignment operator to increment i | Hello 0
Hello 1
Hello 2
Hello 3
Hello 4
Hello 5
Hello 6
Hello 7
Hello 8
Hello 9
Hello 10
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
Application 2 | #Create a Python program that displays integers less than 10 but not less than 3
i = 0
while i<10:
i+=1 #Assignment operator to increment i
if i<3:
continue
if i==10:
break
print(i) | 3
4
5
6
7
8
9
| Apache-2.0 | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 |
Lab 11: MLP -- exercise Understanding the training loop | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from random import randint
import utils | _____no_output_____ | MIT | codes/labs_lecture07/lab01_mlp/.ipynb_checkpoints/mlp_exercise-checkpoint.ipynb | wesleyjtann/Deep-learning-course-CE7454-2018 |
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