# -*- coding: utf-8 -*-
"""Proyecto
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/175o-2viQ5571edB9P-BHId-g5onpE9rH
"""
#Implementación basada en el paper basde del proyecto
#Descargando el Dataset de Kaggle
from google.colab import files
files.upload() #this will prompt you to upload the kaggle.json
!pip install -q kaggle
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!ls ~/.kaggle
!chmod 600 /root/.kaggle/kaggle.json # set permission
! kaggle datasets download -d jessicali9530/celeba-dataset
! unzip celeba-dataset.zip -d kaggle
! unzip kaggle/img_align_celeba.zip -d kaggle
from google.colab import drive
# -*- coding: utf-8 -*-
"""ANN1.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1J0nfogscnNz7ZMlUm4oGZHozPlybmSR0
"""
import pandas as pd
from google.colab import files
file = files.upload()
data = pd.read_csv("breastCancer.csv", header=None)
df = pd.DataFrame(data)
print(df)
df[6] = df[6].replace('?', 0)
df[6] = df[6].astype(int)
print(df.dtypes)
mean = int(df[6].mean())
df[6] = df[6].replace(0, mean)
print(df)
df[10] = df[10].replace(2, 0).replace(4, 1)
print(df)
from sklearn.model_selection import train_test_split
X = df.iloc[:, 1:10]
def read_data(filename,delimiter,encoding='utf-8'): uploaded = files.upload() return pd.read_csv(io.BytesIO(uploaded[filename]),delimiter=delimiter,encoding=encoding)
# Colab library to upload files to notebook
from google.colab import files
from google.colab import drive
drive.mount('/content/drive', force_remount=True)
# Install Kaggle library
!pip install -q kaggle
# Upload kaggle API key file
data = files.upload()
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
# Download the dataset from kaggle
!kaggle datasets download -d tawsifurrahman/covid19-radiography-database
# Extract zipfile
import zipfile
zip_ref = zipfile.ZipFile('covid19-radiography-database.zip', 'r')
zip_ref.extractall('files')
zip_ref.close()
# Modules for train-val split
import os
import numpy as np
import random
import argparse
from shutil import copyfile
> Validation Data (635 Images)
###Mount Gdrive serta Download dan Ekstrak Dataset
"""
from google.colab import drive
drive.mount('/content/drive')
import os
os.environ['KAGGLE_CONFIG_DIR'] = "/content/drive/MyDrive/Dataset/Dataset Citra 240 - 235"
# Commented out IPython magic to ensure Python compatibility.
# %cd /content/drive/MyDrive/Dataset/Dataset Citra 240 - 235
from google.colab import files
files.upload()
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!ls ~/.kaggle
!chmod 600 /root/.kaggle/kaggle.json
!kaggle datasets download -d sartajbhuvaji/brain-tumor-classification-mri
!mkdir 'Dataset Brain MRI'
import zipfile
ekstrak_zip = '/content/drive/MyDrive/Dataset/Dataset Citra 240 - 235/brain-tumor-classification-mri.zip'
out_zip = zipfile.ZipFile(ekstrak_zip, 'r')
out_zip.extractall('/content/drive/MyDrive/Dataset/Dataset Citra 240 - 235/Dataset Brain MRI')
Original file is located at
https://colab.research.google.com/drive/1LEdeNqtc0O35vEemIfiuMkwuSHT-ExlS
"""
#Description : This programs detect breast cancer, based off of data
#import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# load the data
from google.colab import files
uploaded = files.upload()
df = pd.read_csv('data.csv')
df.head(7)
#count of the number of empty values in each column
#count the number of rows and columns in thr datset
df.shape
df.isna().sum()
#drop the column with all the missing values
df = df.dropna(axis=1)
#get the count of the number of rows and columns
df.shape
Original file is located at
https://colab.research.google.com/drive/1twFvSWFXwNLmRb7BIl2DwtL5cAecoRSD
"""
#simple linear regression in python
#step 1:
#importing libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#step 2:
#import dataset:
from google.colab import files
up = files.upload()
data = pd.read_csv('Salary_Data.csv')
#now choose the prediction set i.e y and features i.e X
#X=data.loc["YearsExperience"]
#it doesnt work as it is used to retrieve data of "years experience" under index_col we have specified ie extracting thesingle row
#y=data.loc["Salary"]
#to make it work, we need to specify either the index col while reading csv or use this:
X = data.iloc[:, :-1]
y = data.iloc[:, 1]
print(X)
print(y)
import chardet
import re
import sklearn
import itertools
import emoji
from simpletransformers.classification import ClassificationModel
import torch
from sklearn.impute import SimpleImputer
from sklearn.model_selection import KFold, cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
"""# IMPORT DATASET"""
from google.colab import files
data_to_load = files.upload()
#df_raw = pd.read_csv("C:\\Users\\Administrator\\Desktop\\hsd\\labeled_data.csv")
df_raw = pd.read_csv("labeled_data.csv", encoding='latin1')
#Try calling read_csv with encoding='latin1', encoding='iso-8859-1' or encoding='cp1252'
"""Target is defined as 1 & 0. ‘1’ indicates that it is a preventive information & ‘0’ indicates other wise"""
df_raw.head()
"""# Google Translate abstract data"""
from googletrans import Translator
translator = Translator()
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1-RaUtF2Lx0mhc6UuqkpBeybIhUU1FZgw
"""
# Importing required libraries
import pandas as pd
import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt
#Loading data
from google.colab import files # Use to load data on Google Colab
uploaded = files.upload() # Use to load data on Google Colab
df = pd.read_csv('FB_30_days.csv')
df.head(7)
# Creating lists for X and Y axis
dates = []
prices = []
# No of rows and coluumns
df.shape
# Printing the last row
df.tail(1)
# Storing data in df except the last row (cause we want to predict it)
df = df.head(len(df) - 1)
"""### Monte o Google Drive
Para transmitir arquivos, precisamos montar o Google Drive.
"""
from google.colab import drive
drive.mount("/content/drive")
"""### Adicionar do arquivo Torrent
Você pode executar esta célula para adicionar mais arquivos quantas vezes quiser
"""
from google.colab import files
source = files.upload()
params = {
"save_path": "/content/drive/My Drive/Torrent",
"ti": lt.torrent_info(list(source.keys())[0]),
}
downloads.append(ses.add_torrent(params))
"""### Adicionar Link Magnético"""
params = {"save_path": "/content/drive/My Drive/Torrent"}
while True:
magnet_link = input("Digite o link magnético ou digite Exit: ")
if magnet_link.lower() == "exit":
break
downloads.append(
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from google.colab import files
uploaded = files.upload()
import io
import pandas as pd
df = pd.read_csv(io.BytesIO(uploaded['train.txt']),
delimiter=" ",
names=['x', 'y', 'label'],
header=None)
print(df)
x = df['x'].tolist()
y = df['y'].tolist()
classes = df['label'].tolist()
class1Indexes = []
class2Indexes = []
for i in range(len(classes)):
if classes[i] == 1:
class1Indexes.append(i)
else:
class2Indexes.append(i)
x_class1 = []
x_class2 = []
y_class1 = []
from google.colab import files
import pandas as pd
import io
upload_files = files.upload()
for filename in upload_files.keys():
data = pd.read_csv(io.StringIO(upload_files[filename].decode('utf-8')), header=None)
print(data.head())
for ns in drums_samples:
play(ns)
#@title Optionally download generated MIDI samples.
for i, ns in enumerate(drums_samples):
download(ns, '%s_sample_%d.mid' % (drums_sample_model, i))
"""## Generate Interpolations"""
#@title Option 1: Use example MIDI files for interpolation endpoints.
input_drums_midi_data = [
tf.io.gfile.GFile(fn, mode='rb').read()
for fn in sorted(tf.io.gfile.glob(BASE_DIR + '/midi/drums_2bar*.mid'))]
#@title Option 2: upload your own MIDI files to use for interpolation endpoints instead of those provided.
input_drums_midi_data = files.upload().values() or input_drums_midi_data
#@title Extract drums from MIDI files. This will extract all unique 2-bar drum beats using a sliding window with a stride of 1 bar.
drums_input_seqs = [mm.midi_to_sequence_proto(m) for m in input_drums_midi_data]
extracted_beats = []
for ns in drums_input_seqs:
extracted_beats.extend(drums_nade_full_config.data_converter.from_tensors(
drums_nade_full_config.data_converter.to_tensors(ns)[1]))
for i, ns in enumerate(extracted_beats):
print("Beat", i)
play(ns)
#@title Interpolate between 2 beats, selected from those in the previous cell.
drums_interp_model = "drums_2bar_oh_hikl" #@param ["drums_2bar_oh_lokl", "drums_2bar_oh_hikl", "drums_2bar_nade_reduced", "drums_2bar_nade_full"]
start_beat = 0 #@param {type:"integer"}
end_beat = 1 #@param {type:"integer"}
# -*- coding: utf-8 -*-
"""piechartvisualisation.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1KDBBvrHTzO_PRs5gXV6z3u0zdPCdinla
"""
# Commented out IPython magic to ensure Python compatibility.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# %matplotlib inline
from google.colab import files
dff = files.upload()
df = pd.read_csv('piechart.csv')
print(df)
#data
category = df["category"]
num = df["num"]
color = ['red', 'lightskyblue']
#plot
result = plt.pie(num, labels=category, autopct='%1.1f%%', colors=color)
plt.show()
import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.layers.experimental import preprocessing print(tf.__version__) from google.colab import files uploaded = files.upload() import io train = pd.read_csv(io.BytesIO(uploaded['train.csv'])) train_data = train.copy() train_data train_data.isna().sum() train_data = train_data.dropna() train_data = pd.get_dummies(train_data, prefix='', prefix_sep='') train_data
def upload_image(): uploaded = files.upload() image = imageio.imread(uploaded[list(uploaded.keys())[0]]) return transform.resize(image, [128, 128])
from sklearn import metrics
import matplotlib.pyplot as plt
import random
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
"""**Next few lines are needed to integrate the kaggle dataset ([Google Play Store Apps](https://www.kaggle.com/lava18/google-play-store-apps)) into google colab**"""
!pip install --quiet kaggle
from google.colab import files
files.upload() #upload the json file that contains api key from kaggle account
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/.
!chmod 600 ~/.kaggle/kaggle.json #altering permissions
!kaggle datasets download -d lava18/google-play-store-apps #this is the api of the dataset obtained from kaggle
from zipfile import ZipFile
zip_file= ZipFile('google-play-store-apps.zip') #this downloaded zip file contains three csv file
data=pd.read_csv(zip_file.open('googleplaystore.csv')) #we choose the googleplaystore.csv and load it into a dataframe called 'data' using pandas
data.head() #prints first 5 entries of the dataframe
data.info() #result shows there are 10841 entries in the dataframe , it also lists the columns present in the dataset
Original file is located at
https://colab.research.google.com/drive/1vYyWiR6FsQFVmzTcEbTiTaInyjBbChsG
"""
# Load the Drive helper and mount
from google.colab import drive
# This will prompt for authorization.
drive.mount('/content/drive')
!rm mywarper.py
!rm ae.py
from google.colab import files
src = list(files.upload().values())[0]
!pip install torch
!pip install torchvision
!pip install --no-cache-dir -I pillow
!pip install imageio
import os
import numpy as np
import argparse
import matplotlib.pyplot as plt
import skimage
from skimage import io, transform
import scipy.io as sio
from mywarper import warp
from datetime import date
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
#Taking Input
stname = input("Enter stock symbol : ")
#Fetch the data
#data = get_history(symbol= stname, start=date(2018,7,12), end=date(2020,7,12))
#data.reset_index()
#Was unable to fetch the data
#Load the data
from google.colab import files
uploaded = files.upload() #upload a csv file
for fn in uploaded.keys():
print('Uploaded file "{name}" with length {length} bytes'.format(
name=fn, length=len(uploaded[fn])))
File = fn
# Store the data
stock = pd.read_csv(File)
#Set the index
stock = stock.set_index(pd.DatetimeIndex(stock['Date'].values))
StartDate = stock.iat[0,2]
EndDate = stock.iat[-1,2]
#Show the Data
# -*- coding: utf-8 -*-
"""fourthbitperdiction.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/15dh01gtTgEBM6PkbijXY8hjvCP5-c-Tu
"""
#import the library pandas as pd
import pandas as pd
import numpy as np
from google.colab import files
upload = files.upload()
df = pd.read_csv('CompleteDataset (2).csv')
pd.read_csv('CompleteDataset (2).csv')
#create a dataframe with all training data except the target column
#and here
X = df.iloc[:, 0:16].values
Y = df.iloc[:, 19].values
print(X)
print(Y)
from sklearn import datasets, linear_model
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1)
import numpy as np
import matplotlib.pyplot as plt
from google.colab import files
from io import BytesIO
from PIL import Image
import tensorflow as tf
from tensorflow import keras
upl = files.upload()
img = Image.open(BytesIO(upl['img.jpg']))
img_style = Image.open(BytesIO(upl['img_style.jpg']))
plt.subplot(1, 2, 1)
plt.imshow(img)
plt.subplot(1, 2, 2)
plt.imshow(img_style)
plt.show()
x_img = keras.applications.vgg19.preprocess_input(np.expand_dims(img, axis=0))
x_style = keras.applications.vgg19.preprocess_input(
np.expand_dims(img_style, axis=0))
def deprocess_img(processed_img):
x = processed_img.copy()
if len(x.shape) == 4:
x = np.squeeze(x, 0)
assert len(x.shape) == 3, (
"Input to deprocess image must be an image of "
"dimension [1, height, width, channel] or [height, width, channel]")
# coding: UTF-8
"""
2018.5.2
Colaboratoryファイル入出力
"""
# アップロード
from google.colab import files
uploaded = files.upload()
# ファイル入力
with open("input.csv", 'r') as f:
print(f.read())
# ファイル出力
with open("output.txt", "w") as f:
f.write("Nyanhello\nworld\n")
# 確認
f = open('output.txt', 'r')
print(f.read())
# ダウンロード
from google.colab import files
files.download('output.txt')
iris = load_iris()
iris_frame = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
columns=iris['feature_names'] + ['target'])
iris_frame['target'] = iris_frame['target'].map({
1: "versicolor",
0: "setosa",
2: "virginica"
})
X = iris_frame.iloc[:, :-1]
Y = iris_frame.iloc[:, [-1]]
iris_frame
"""우리는 이 데이터의 많은 feature 중 sepal에 관련된 두 개의 feature만 이용해서 학습을 할 것이다. 두개의 feature만 선택하는 이유는 visualization이 비교적 편리하기 때문이다. 따라서 이 데이터들을 의사 결정 나무 모델을 통해서 학습하고 각 점들을 예측한 후 이 결과를 2차원으로 visualizing함으로써 decision boundary를 확인해보겠다. 우선 가장 디폴트 상태부터 차례대로 파라미터를 변경해가면서 실습을 진행해보도록 하겠다."""
from google.colab import files
uploaded = files.upload() # 파일 업로드 기능 실행
for fn in uploaded.keys(): # 업로드된 파일 정보 출력
print('User uploaded file "{name}" with length {length} bytes'.format(
name=fn, length=len(uploaded[fn])))
"""* [random_state=0, criterion = ‘gini’, splitter = ‘best’, max_depth=5]"""
from sklearn.tree import DecisionTreeClassifier
##아래 하이퍼파라미터 수정으로 결과 확인
clf = DecisionTreeClassifier(random_state=0, criterion='gini', max_depth=5)
import matplotlib.colors as colors
df1 = iris_frame[["sepal length (cm)", "sepal width (cm)", "target"]]
X = df1.iloc[:, 0:2]
Y = df1.iloc[:, 2].replace({