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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--- Step 6: Test Your AlgorithmIn this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that _you_ look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog? (IMPLEMENTATION) Test Your A... | ## COMPLETED: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
## suggested code, below
for file in np.hstack((human_files[:3], dog_files[:3])):
run_app(file)
import numpy as np
from glob import glob
# load filenames
files = np.array(gl... | _____no_output_____ | MIT | dog_app.ipynb | blackcisne10/Dog-Breed-Classifier |
VacationPy---- Note* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps. | # Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import gmaps
import os
# Import API key
api_key = "c280ce5700cf1679dcce087b5b74f838" | _____no_output_____ | ADSL | starter_code/VacationPy.ipynb | DiamondN97/python-api-challenge |
Store Part I results into DataFrame* Load the csv exported in Part I to a DataFrame | weather_data = pd.read_csv('')
| _____no_output_____ | ADSL | starter_code/VacationPy.ipynb | DiamondN97/python-api-challenge |
Humidity Heatmap* Configure gmaps.* Use the Lat and Lng as locations and Humidity as the weight.* Add Heatmap layer to map. Create new DataFrame fitting weather criteria* Narrow down the cities to fit weather conditions.* Drop any rows will null values. Hotel Map* Store into variable named `hotel_df`.* Add a "Hotel ... | # NOTE: Do not change any of the code in this cell
# Using the template add the hotel marks to the heatmap
info_box_template = """
<dl>
<dt>Name</dt><dd>{Hotel Name}</dd>
<dt>City</dt><dd>{City}</dd>
<dt>Country</dt><dd>{Country}</dd>
</dl>
"""
# Store the DataFrame Row
# NOTE: be sure to update with your DataFrame na... | _____no_output_____ | ADSL | starter_code/VacationPy.ipynb | DiamondN97/python-api-challenge |
UNIVERSIDADE FEDERAL DO PIAUÍCURSO DE GRADUAÇÃO EM ENGENHARIA ELÉTRICADISCIPLINA: TÉCNICAS DE OTIMIZAÇÃODOCENTE: ALDIR SILVA SOUSADISCENTE: MARIANA DE SOUSA MOURAAtividade 3: Otimização Irrestrita pelo Método de Newton MultivariávelResolva os exercícios usando o método de Descida Gradiente. **Método da Descida Gradie... | import numpy as np
import sympy as sym #Para criar variáveis simbólicas.
class Params:
def __init__(self,f,vars,eps,a,b):
self.f = f
self.a = a
self.b = b
self.vars = vars #variáveis simbólicas
self.eps = eps
def eval(sym_f,vars,x):
map = dict()
map[vars[0]] = x
return sym_f.subs(... | _____no_output_____ | MIT | Exercicios_Steepest_Descent.ipynb | marianasmoura/tecnicas-de-otimizacao |
Calcula o gradiente e permite a substituição de valores em variáveis nas funções | # Função para o cálculo do gradiente
import sympy as sym #Para criar variáveis simbólicas.
def gradiente_simbolico(funcao,variaveis):
g1 = [sym.diff(funcao,x) for x in variaveis]
return g1
# Função para substituição dos valores nas variáveis simbólicas
def eval_simbolica(f,variaveis,x):
mp = dict()
fo... | _____no_output_____ | MIT | Exercicios_Steepest_Descent.ipynb | marianasmoura/tecnicas-de-otimizacao |
Parâmetros que serõ utilizados pela função | import numpy as np
import sympy as sym
class Parametros:
def __init__(self,f,d1f,vars,m,eps,nmax):
self.f = f
self.d1f = d1f
self.m = m
self.eps = eps
self.nmax = nmax
self.vars = vars | _____no_output_____ | MIT | Exercicios_Steepest_Descent.ipynb | marianasmoura/tecnicas-de-otimizacao |
Código para a otimização a partir da Descida Gradiente | import pandas as pd
import math
lmbd = sym.Symbol('lmbd')
def steepestDescent(p):
f = p.f
d1f = p.d1f
m = p.m
eps = p.eps
vars= p.vars
nmax = p.nmax
k = 0
v = [0 for i in vars]
cols = ['x','grad(x)','lambda']
table = pd.DataFrame([], columns=cols)... | _____no_output_____ | MIT | Exercicios_Steepest_Descent.ipynb | marianasmoura/tecnicas-de-otimizacao |
**1.** Considere o seguinte problema:Minimizar $\sum_{i=2}^{n} [100(x_i-x^2_{i-1})^2 + (1-x_{i-1})^2]$Resolva para n = 5, 10, e 50. Iniciando do ponto $x_0 = [-1.2,1.0,-1.2,1.0,...]$ | # Para n = 5
import numpy as np
import sympy as sym
variaveis = list(sym.symbols("x:5"))
c = variaveis
def f1(c):
fo = 0
for i in range(1,5):
fo = fo + 100*(c[i] - c[i-1]**2)**2 + (1 - c[i-1])**2
return fo
x = []
for i in range(1,6):
if (i%2 != 0):
x.append(-1.2)
else:
... | _____no_output_____ | MIT | Exercicios_Steepest_Descent.ipynb | marianasmoura/tecnicas-de-otimizacao |
**2.** Resolva:Minimizar $(x_1 - x^3_2)^2 + 3(x_1 - x_2)^4$ | import numpy as np
import sympy as sym
x1 = sym.Symbol('x1')
x2 = sym.Symbol('x2')
variaveis = [x1,x2]
c = variaveis
fo = 0
def fo(c):
return (c[0] - c[1]**3)**2 + 3*(c[0] - c[1])**4
x = [1.2,1.5]
eps = 1e-3
nmax = 500
d1f = gradiente_simbolico(fo(c),c)
p = Parametros(fo,d1f,c,x,eps,nmax)
m,df = steepestDesc... | _____no_output_____ | MIT | Exercicios_Steepest_Descent.ipynb | marianasmoura/tecnicas-de-otimizacao |
**3.** Resolva:$2(x_1 - 2)^4 + (2x_1 - x_2)^2 = 4$ | import numpy as np
import sympy as sym
x1 = sym.Symbol('x1')
x2 = sym.Symbol('x2')
variaveis = [x1,x2]
c = variaveis
fo = 0
def fo(c):
return (2*(c[0] - 2)**4 + (2*c[0] - c[1])**2 - 4)**2
x = [1.2,0.5]
eps = 1e-3
nmax = 300
d1f = gradiente_simbolico(fo(c),c)
p = Parametros(fo,d1f,c,x,eps,nmax)
m,df = steepes... | _____no_output_____ | MIT | Exercicios_Steepest_Descent.ipynb | marianasmoura/tecnicas-de-otimizacao |
**4** Resolva:Minimizar $(x_1 - 3)^4 + (x_1 - 3x_2)^2$ | import numpy as np
import sympy as sym
x1 = sym.Symbol('x1')
x2 = sym.Symbol('x2')
variaveis = [x1,x2]
c = variaveis
def f4(c):
return (c[0] - 3)**4 + (c[0] - 3*c[1])**2
x = [1.5,1]
eps = 1e-4
nmax = 300
d1f = gradiente_simbolico(f4(c),c)
p = Parametros(f4,d1f,c,x,eps,nmax)
n,t = steepestDescent(p)
t
n | _____no_output_____ | MIT | Exercicios_Steepest_Descent.ipynb | marianasmoura/tecnicas-de-otimizacao |
Building your Deep Neural Network: Step by StepWelcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want!- In this notebook, you will implement all the functions re... | import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases_v4 import *
from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['imag... | The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
| MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
2 - Outline of the AssignmentTo build your neural network, you will be implementing several "helper functions". These helper functions will be used in the next assignment to build a two-layer neural network and an L-layer neural network. Each small helper function you will implement will have detailed instructions tha... | # GRADED FUNCTION: initialize_parameters
def initialize_parameters(n_x, n_h, n_y):
"""
Argument:
n_x -- size of the input layer
n_h -- size of the hidden layer
n_y -- size of the output layer
Returns:
parameters -- python dictionary containing your parameters:
W1 --... | W1 = [[ 0.01624345 -0.00611756 -0.00528172]
[-0.01072969 0.00865408 -0.02301539]]
b1 = [[ 0.]
[ 0.]]
W2 = [[ 0.01744812 -0.00761207]]
b2 = [[ 0.]]
| MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
**Expected output**: **W1** [[ 0.01624345 -0.00611756 -0.00528172] [-0.01072969 0.00865408 -0.02301539]] **b1** [[ 0.] [ 0.]] **W2** [[ 0.01744812 -0.00761207]] **b2** [[ 0.]] 3.2 - L-layer Neural NetworkThe initialization for a deeper L-layer neural n... | # GRADED FUNCTION: initialize_parameters_deep
def initialize_parameters_deep(layer_dims):
"""
Arguments:
layer_dims -- python array (list) containing the dimensions of each layer in our network
Returns:
parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
... | W1 = [[ 0.01788628 0.0043651 0.00096497 -0.01863493 -0.00277388]
[-0.00354759 -0.00082741 -0.00627001 -0.00043818 -0.00477218]
[-0.01313865 0.00884622 0.00881318 0.01709573 0.00050034]
[-0.00404677 -0.0054536 -0.01546477 0.00982367 -0.01101068]]
b1 = [[ 0.]
[ 0.]
[ 0.]
[ 0.]]
W2 = [[-0.01185047 -0.002056... | MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
**Expected output**: **W1** [[ 0.01788628 0.0043651 0.00096497 -0.01863493 -0.00277388] [-0.00354759 -0.00082741 -0.00627001 -0.00043818 -0.00477218] [-0.01313865 0.00884622 0.00881318 0.01709573 0.00050034] [-0.00404677 -0.0054536 -0.01546477 0.00982367 -0.01101068]] **b1** [[ 0... | # GRADED FUNCTION: linear_forward
def linear_forward(A, W, b):
"""
Implement the linear part of a layer's forward propagation.
Arguments:
A -- activations from previous layer (or input data): (size of previous layer, number of examples)
W -- weights matrix: numpy array of shape (size of current la... | Z = [[ 3.26295337 -1.23429987]]
| MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
**Expected output**: **Z** [[ 3.26295337 -1.23429987]] 4.2 - Linear-Activation ForwardIn this notebook, you will use two activation functions:- **Sigmoid**: $\sigma(Z) = \sigma(W A + b) = \frac{1}{ 1 + e^{-(W A + b)}}$. We have provided you with the `sigmoid` function. This function returns **two** ... | # GRADED FUNCTION: linear_activation_forward
def linear_activation_forward(A_prev, W, b, activation):
"""
Implement the forward propagation for the LINEAR->ACTIVATION layer
Arguments:
A_prev -- activations from previous layer (or input data): (size of previous layer, number of examples)
W -- weigh... | With sigmoid: A = [[ 0.96890023 0.11013289]]
With ReLU: A = [[ 3.43896131 0. ]]
| MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
**Expected output**: **With sigmoid: A ** [[ 0.96890023 0.11013289]] **With ReLU: A ** [[ 3.43896131 0. ]] **Note**: In deep learning, the "[LINEAR->ACTIVATION]" computation is counted as a single layer in the neural network, not two layers. d) L-Layer Model For even more c... | # GRADED FUNCTION: L_model_forward
def L_model_forward(X, parameters):
"""
Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation
Arguments:
X -- data, numpy array of shape (input size, number of examples)
parameters -- output of initialize_parameters_deep()
... | AL = [[ 0.03921668 0.70498921 0.19734387 0.04728177]]
Length of caches list = 3
| MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
**AL** [[ 0.03921668 0.70498921 0.19734387 0.04728177]] **Length of caches list ** 3 Great! Now you have a full forward propagation that takes the input X and outputs a row vector $A^{[L]}$ containing your predictions. It also records all intermediate values in "caches". Using $A^{[L]}$... | # GRADED FUNCTION: compute_cost
def compute_cost(AL, Y):
"""
Implement the cost function defined by equation (7).
Arguments:
AL -- probability vector corresponding to your label predictions, shape (1, number of examples)
Y -- true "label" vector (for example: containing 0 if non-cat, 1 if cat), sh... | cost = 0.41493159961539694
| MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
**Expected Output**: **cost** 0.41493159961539694 6 - Backward propagation moduleJust like with forward propagation, you will implement helper functions for backpropagation. Remember that back propagation is used to calculate the gradient of the loss function with respect to the parameters. **Reminder... | # GRADED FUNCTION: linear_backward
def linear_backward(dZ, cache):
"""
Implement the linear portion of backward propagation for a single layer (layer l)
Arguments:
dZ -- Gradient of the cost with respect to the linear output (of current layer l)
cache -- tuple of values (A_prev, W, b) coming from ... | dA_prev = [[ 0.51822968 -0.19517421]
[-0.40506361 0.15255393]
[ 2.37496825 -0.89445391]]
dW = [[-0.10076895 1.40685096 1.64992505]]
db = [[ 0.50629448]]
| MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
**Expected Output**: **dA_prev** [[ 0.51822968 -0.19517421] [-0.40506361 0.15255393] [ 2.37496825 -0.89445391]] **dW** [[-0.10076895 1.40685096 1.64992505]] **db** [[ 0.50629448]] 6.2 - Linear-Activation backwardNext, you will create a... | # GRADED FUNCTION: linear_activation_backward
def linear_activation_backward(dA, cache, activation):
"""
Implement the backward propagation for the LINEAR->ACTIVATION layer.
Arguments:
dA -- post-activation gradient for current layer l
cache -- tuple of values (linear_cache, activation_cache)... | sigmoid:
dA_prev = [[ 0.11017994 0.01105339]
[ 0.09466817 0.00949723]
[-0.05743092 -0.00576154]]
dW = [[ 0.10266786 0.09778551 -0.01968084]]
db = [[-0.05729622]]
relu:
dA_prev = [[ 0.44090989 -0. ]
[ 0.37883606 -0. ]
[-0.2298228 0. ]]
dW = [[ 0.44513824 0.37371418 -0.10478989]]
db = [[-0... | MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
**Expected output with sigmoid:** dA_prev [[ 0.11017994 0.01105339] [ 0.09466817 0.00949723] [-0.05743092 -0.00576154]] dW [[ 0.10266786 0.09778551 -0.01968084]] db [[-0.05729622]] **Expected output with relu:** dA_prev ... | # GRADED FUNCTION: L_model_backward
def L_model_backward(AL, Y, caches):
"""
Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group
Arguments:
AL -- probability vector, output of the forward propagation (L_model_forward())
Y -- true "label" vector (contain... | dW1 = [[ 0.41010002 0.07807203 0.13798444 0.10502167]
[ 0. 0. 0. 0. ]
[ 0.05283652 0.01005865 0.01777766 0.0135308 ]]
db1 = [[-0.22007063]
[ 0. ]
[-0.02835349]]
dA1 = [[ 0.12913162 -0.44014127]
[-0.14175655 0.48317296]
[ 0.01663708 -0.05670698]]
| MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
**Expected Output** dW1 [[ 0.41010002 0.07807203 0.13798444 0.10502167] [ 0. 0. 0. 0. ] [ 0.05283652 0.01005865 0.01777766 0.0135308 ]] db1 [[-0.22007063] [ 0. ] [-0.02835349]] dA1 [[ 0.12913162 -0.44... | # GRADED FUNCTION: update_parameters
def update_parameters(parameters, grads, learning_rate):
"""
Update parameters using gradient descent
Arguments:
parameters -- python dictionary containing your parameters
grads -- python dictionary containing your gradients, output of L_model_backward
... | W1 = [[-0.59562069 -0.09991781 -2.14584584 1.82662008]
[-1.76569676 -0.80627147 0.51115557 -1.18258802]
[-1.0535704 -0.86128581 0.68284052 2.20374577]]
b1 = [[-0.04659241]
[-1.28888275]
[ 0.53405496]]
W2 = [[-0.55569196 0.0354055 1.32964895]]
b2 = [[-0.84610769]]
| MIT | deepl/Building+your+Deep+Neural+Network+-+Step+by+Step+v8.ipynb | stepinski/machinelearning |
Strategy Based on Labels vs BH | dfDetail = pd.read_csv("./GOOGL_weekly_return_volatility_detailed.csv")
dfYear2 = dfDetail[dfDetail.Year == 2020]
year2.label = labelYear2
## Add label to detail
labelMap = {}
for (y, w, l) in zip(year2.Year, year2.Week_Number, year2.label):
key = (y, w)
value = l
labelMap[key] = value
temp = []
for (y, w... | Using Label: 215.85697376702737
Buy on first day and Sell on last day: 121.17033527942765
| MIT | Assignment_5/kNN_logistic_stocks/.ipynb_checkpoints/KNN-checkpoint.ipynb | KyleLeePiupiupiu/CS677_Assignment |
2.3 Arithmetic Multiplication (`*`) | 7 * 4 | _____no_output_____ | MIT | examples/ch02/snippets_ipynb/02_03.ipynb | eltechno/python_course |
Exponentiation (`**`) | 2 ** 10
9 ** (1 / 2) | _____no_output_____ | MIT | examples/ch02/snippets_ipynb/02_03.ipynb | eltechno/python_course |
True Division (`/`) vs. Floor Division (`//`) | 7 / 4
7 // 4
3 // 5
14 // 7
-13 / 4
-13 // 4 | _____no_output_____ | MIT | examples/ch02/snippets_ipynb/02_03.ipynb | eltechno/python_course |
Exceptions and Tracebacks | 123 / 0
z + 7 | _____no_output_____ | MIT | examples/ch02/snippets_ipynb/02_03.ipynb | eltechno/python_course |
Remainder Operator | 17 % 5
7.5 % 3.5 | _____no_output_____ | MIT | examples/ch02/snippets_ipynb/02_03.ipynb | eltechno/python_course |
Grouping Expressions with Parentheses | 10 * (5 + 3)
10 * 5 + 3
##########################################################################
# (C) Copyright 2019 by Deitel & Associates, Inc. and #
# Pearson Education, Inc. All Rights Reserved. #
# ... | _____no_output_____ | MIT | examples/ch02/snippets_ipynb/02_03.ipynb | eltechno/python_course |
Task 11 - Motor Control Introduction to modeling and simulation of human movementhttps://github.com/BMClab/bmc/blob/master/courses/ModSim2018.md * Task (for Lecture 11):Change the derivative of the contractile element length function. The new function must compute the derivative according to the article from Thelen(20... | import numpy as np
#import pandas as pd
import matplotlib.pyplot as plt
import math
%matplotlib notebook | _____no_output_____ | MIT | courses/modsim2018/tasks/Desiree/Task11_MotorControl.ipynb | desireemiraldo/bmc |
Muscle properties | Lslack = .223
Umax = .04
Lce_o = .093 #optmal l
width = .63#*Lce_o
Fmax = 3000
a = 1
#b = .25*10#*Lce_o | _____no_output_____ | MIT | courses/modsim2018/tasks/Desiree/Task11_MotorControl.ipynb | desireemiraldo/bmc |
Initial conditions | Lnorm_ce = .087/Lce_o #norm
t0 = 0
tf = 2.99
h = 1e-3
t = np.arange(t0,tf,h)
F = np.empty(t.shape)
Fkpe = np.empty(t.shape)
FiberLen = np.empty(t.shape)
TendonLen = np.empty(t.shape) | _____no_output_____ | MIT | courses/modsim2018/tasks/Desiree/Task11_MotorControl.ipynb | desireemiraldo/bmc |
Simulation - Series for i in range (len(t)): ramp if t[i]<=1: Lm = 0.31 elif t[i]>1 and t[i]<2: Lm = .31 + .1*(t[i]-1) print(Lm) shortening at 4cm/s Lsee = Lm - Lce if Lsee<Lslack: F[i] = 0 else: F[i] = Fmax*((Lsee-Lslack)/(Umax*Lslack))**2 ... | def TendonForce (Lnorm_see,Lslack, Lce_o):
'''
Compute tendon force
Inputs:
Lnorm_see = normalized tendon length
Lslack = slack length of the tendon (non-normalized)
Lce_o = optimal length of the fiber
Output:
Fnorm_tendon = normalized tendon force
'''
... | _____no_output_____ | MIT | courses/modsim2018/tasks/Desiree/Task11_MotorControl.ipynb | desireemiraldo/bmc |
def ContractileElementDot(F0, Fnorm_CE, a, b): ''' Compute Contractile Element Derivative Inputs: F0 = Force-Length Curve Fce = Contractile element force Output: Lnorm_cedot = normalized contractile element length derivative ''' if Fnorm_CE>F0: print('Error: cannot do... | def ContractileElementDot(F0, Fnorm_CE, a):
'''
Compute Contractile Element Derivative
Inputs:
F0 = Force-Length Curve
Fce = Contractile element force
Output:
Lnorm_cedot = normalized contractile element length derivative
'''
FMlen = 1.4 # young adults
... | _____no_output_____ | MIT | courses/modsim2018/tasks/Desiree/Task11_MotorControl.ipynb | desireemiraldo/bmc |
Simulation - Parallel | #Normalizing
for i in range (len(t)):
#ramp
if t[i]<=1:
Lm = 0.31
elif t[i]>1 and t[i]<2:
Lm = .31 - .04*(t[i]-1)
#print(Lm)
#shortening at 4cm/s
Lnorm_see = tendonLength(Lm,Lce_o,Lnorm_ce)
Fnorm_tendon = TendonForce (Lnorm_see,Lslack, Lce_o)
Fnorm_k... | _____no_output_____ | MIT | courses/modsim2018/tasks/Desiree/Task11_MotorControl.ipynb | desireemiraldo/bmc |
Plots | fig, ax = plt.subplots(1, 1, figsize=(6,6), sharex=True)
ax.plot(t,F,c='red')
plt.grid()
plt.xlabel('time (s)')
plt.ylabel('Force (N)')
ax.legend()
fig, ax = plt.subplots(1, 1, figsize=(6,6), sharex=True)
ax.plot(t,FiberLen, label = 'fiber')
ax.plot(t,TendonLen, label = 'tendon')
plt.grid()
plt.xlabel('time (s)')
p... | _____no_output_____ | MIT | courses/modsim2018/tasks/Desiree/Task11_MotorControl.ipynb | desireemiraldo/bmc |
Creation of annotations from unannotated noise recordings Purpose of this notebookThis notebook describes the steps involved in automatically creating noise annotations from non-annotated noise recordings. This notebook is used for creating noise annotations from data provided by the Universty of Aberdeen in Scotland.... | from ecosound.core.annotation import Annotation
from ecosound.core.metadata import DeploymentInfo
from ecosound.core.audiotools import Sound
from ecosound.core.tools import filename_to_datetime
import os
import pandas as pd
import numpy as np
import uuid
from datetime import datetime
def create_noise_annot(audio_dir, ... | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Dataset 1: UK-UAberdeen-MorayFirth-201904_986-110Definition of all the paths of all folders with the raw annotation and audio files for this deployment. | audio_dir = r'C:\Users\xavier.mouy\Documents\GitHub\minke-whale-dataset\datasets\UK-UAberdeen-MorayFirth-201904_986-110'
deployment_file = r'deployment_info.csv'
file_ext = 'wav'
annot_dur_sec = 60 # duration of the noise annotations in seconds
label_class = 'NN' # label to use for the noise class
label_subclass = ... | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Now we can create annotations for all audio files in that folder. | annot = create_noise_annot(audio_dir, deployment_file, file_ext, annot_dur_sec, label_class, label_subclass) | Depl986_1678036995.190402110017.wav
Depl986_1678036995.190406225930.wav
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| Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Let's look at the summary of annotations that were created: | annot.summary() | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
The dataset can now be saved as a Raven annotation file and netcdf4 file: | annot.to_netcdf(os.path.join(audio_dir, 'Annotations_dataset_' + annot.data['deployment_ID'][0] +' annotations.nc'))
annot.to_raven(audio_dir, outfile='Annotations_dataset_' + annot.data['deployment_ID'][0] +'.Table.1.selections.txt', single_file=True) | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Here is what the annotations look like in Raven: Dataset 2: UK-UAberdeen-MorayFirth-201904_1027-235Definition of all the paths of all folders with the raw annotation and audio files for this deployment. | audio_dir = r'C:\Users\xavier.mouy\Documents\GitHub\minke-whale-dataset\datasets\UK-UAberdeen-MorayFirth-201904_1027-235'
deployment_file = r'deployment_info.csv'
file_ext = 'wav'
annot_dur_sec = 60 # duration of the noise annotations in seconds
label_class = 'NN' # label to use for the noise class
label_subclass =... | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Now we can create annotations for all audio files in that folder. | annot = create_noise_annot(audio_dir, deployment_file, file_ext, annot_dur_sec, label_class, label_subclass) | Depl1027_1677725722.190403115956.wav
Depl1027_1677725722.190411055855.wav
Depl1027_1677725722.190415235822.wav
| Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Let's look at the summary of annotations that were created: | annot.summary() | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
The dataset can now be saved as a Raven annotation file and netcdf4 file: | annot.to_netcdf(os.path.join(audio_dir, 'Annotations_dataset_' + annot.data['deployment_ID'][0] +' annotations.nc'))
annot.to_raven(audio_dir, outfile='Annotations_dataset_' + annot.data['deployment_ID'][0] +'.Table.1.selections.txt', single_file=True) | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Dataset 3: UK-UAberdeen-MorayFirth-201904_1029-237Definition of all the paths of all folders with the raw annotation and audio files for this deployment. | audio_dir = r'C:\Users\xavier.mouy\Documents\GitHub\minke-whale-dataset\datasets\UK-UAberdeen-MorayFirth-201904_1029-237'
deployment_file = r'deployment_info.csv'
file_ext = 'wav'
annot_dur_sec = 60 # duration of the noise annotations in seconds
label_class = 'NN' # label to use for the noise class
label_subclass =... | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Now we can create annotations for all audio files in that folder. | annot = create_noise_annot(audio_dir, deployment_file, file_ext, annot_dur_sec, label_class, label_subclass) | Depl1029_134541352.190403235927.wav
Depl1029_134541352.190404175922.wav
Depl1029_134541352.190409115847.wav
| Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Let's look at the summary of annotations that were created: | annot.summary() | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
The dataset can now be saved as a Raven annotation file and netcdf4 file: | annot.to_netcdf(os.path.join(audio_dir, 'Annotations_dataset_' + annot.data['deployment_ID'][0] +' annotations.nc'))
annot.to_raven(audio_dir, outfile='Annotations_dataset_' + annot.data['deployment_ID'][0] +'.Table.1.selections.txt', single_file=True) | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Dataset 4: UK-UAberdeen-MorayFirth-202001_1092-112 (seismic)Definition of all the paths of all folders with the raw annotation and audio files for this deployment. | audio_dir = r'C:\Users\xavier.mouy\Documents\GitHub\minke-whale-dataset\datasets\UK-UAberdeen-MorayFirth-202001_1092-112'
deployment_file = r'deployment_info.csv'
file_ext = 'wav'
annot_dur_sec = 60 # duration of the noise annotations in seconds
label_class = 'NN' # label to use for the noise class
label_subclass =... | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Now we can create annotations for all audio files in that folder. | annot = create_noise_annot(audio_dir, deployment_file, file_ext, annot_dur_sec, label_class, label_subclass) | Depl1092_1678036995.200101014914.wav
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Depl1092_1678036995.2001... | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Let's look at the summary of annotations that were created: | annot.summary() | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
The dataset can now be saved as a Raven annotation file and netcdf4 file: | annot.to_netcdf(os.path.join(audio_dir, 'Annotations_dataset_' + annot.data['deployment_ID'][0] +' annotations.nc'))
annot.to_raven(audio_dir, outfile='Annotations_dataset_' + annot.data['deployment_ID'][0] +'.Table.1.selections.txt', single_file=True) | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Dataset 5: UK-UAberdeen-MorayFirth-202001_1093-164 (seismic)Definition of all the paths of all folders with the raw annotation and audio files for this deployment. | audio_dir = r'C:\Users\xavier.mouy\Documents\GitHub\minke-whale-dataset\datasets\UK-UAberdeen-MorayFirth-202001_1093-164'
deployment_file = r'deployment_info.csv'
file_ext = 'wav'
annot_dur_sec = 60 # duration of the noise annotations in seconds
label_class = 'NN' # label to use for the noise class
label_subclass =... | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Now we can create annotations for all audio files in that folder. | annot = create_noise_annot(audio_dir, deployment_file, file_ext, annot_dur_sec, label_class, label_subclass) | Depl1093_1677725722.200104205913.wav
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Depl1093_1677725722.2001... | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Let's look at the summary of annotations that were created: | annot.summary() | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
The dataset can now be saved as a Raven annotation file and netcdf4 file: | annot.to_netcdf(os.path.join(audio_dir, 'Annotations_dataset_' + annot.data['deployment_ID'][0] +' annotations.nc'))
annot.to_raven(audio_dir, outfile='Annotations_dataset_' + annot.data['deployment_ID'][0] +'.Table.1.selections.txt', single_file=True) | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Dataset 6: UK-UAberdeen-MorayFirth-202101_1136-164Definition of all the paths of all folders with the raw annotation and audio files for this deployment. | audio_dir = r'C:\Users\xavier.mouy\Documents\GitHub\minke-whale-dataset\datasets\UK-UAberdeen-MorayFirth-202101_1136-164'
deployment_file = r'deployment_info.csv'
file_ext = 'wav'
annot_dur_sec = 60 # duration of the noise annotations in seconds
label_class = 'NN' # label to use for the noise class
label_subclass =... | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Now we can create annotations for all audio files in that folder. | annot = create_noise_annot(audio_dir, deployment_file, file_ext, annot_dur_sec, label_class, label_subclass) | Depl1136_1677725722.210102130002.wav
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Depl1136_1677725722.2102... | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Let's look at the summary of annotations that were created: | annot.summary() | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
The dataset can now be saved as a Raven annotation file and netcdf4 file: | annot.to_netcdf(os.path.join(audio_dir, 'Annotations_dataset_' + annot.data['deployment_ID'][0] +' annotations.nc'))
annot.to_raven(audio_dir, outfile='Annotations_dataset_' + annot.data['deployment_ID'][0] +'.Table.1.selections.txt', single_file=True) | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Dataset 7: UK-UAberdeen-MorayFirth-202101_1137-112Definition of all the paths of all folders with the raw annotation and audio files for this deployment. | audio_dir = r'C:\Users\xavier.mouy\Documents\GitHub\minke-whale-dataset\datasets\UK-UAberdeen-MorayFirth-202101_1137-112'
deployment_file = r'deployment_info.csv'
file_ext = 'wav'
annot_dur_sec = 60 # duration of the noise annotations in seconds
label_class = 'NN' # label to use for the noise class
label_subclass =... | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Now we can create annotations for all audio files in that folder. | annot = create_noise_annot(audio_dir, deployment_file, file_ext, annot_dur_sec, label_class, label_subclass) | Depl1137_1678508072.210107040002.wav
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Depl1137_1678508072.2101... | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
Let's look at the summary of annotations that were created: | annot.summary() | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
The dataset can now be saved as a Raven annotation file and netcdf4 file: | annot.to_netcdf(os.path.join(audio_dir, 'Annotations_dataset_' + annot.data['deployment_ID'][0] +' annotations.nc'))
annot.to_raven(audio_dir, outfile='Annotations_dataset_' + annot.data['deployment_ID'][0] +'.Table.1.selections.txt', single_file=True) | _____no_output_____ | Apache-2.0 | dataset_preparation_noise.ipynb | xaviermouy/minke-whale-dataset |
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Dropout
from tensorflow.keras.utils import to_categorical
# Load mnist... | _____no_output_____ | MIT | Neural Networks/03_Deep_Net_in_tensorflow_mnist_classification.ipynb | kishore145/AI-ML-Foundations | |
CT scan UNet demoThis notebook creates a UNet for a minified dataset of animal CTs.If you are on Google Colab, make this train quicker by swapping to a GPU runtime. This is done by clicking `Runtime`, then `Change runtime type`, then selecting `GPU`:![01](https://github.com/pymedphys/pymedphys/blob/85b8434dc2f11bf20b3... | import pathlib
import urllib.request
import shutil
import collections
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras.backend as K
import imageio
import skimage.filters
zip_url = 'https://zenodo.org/record/4448689/files/minified-animal-patient-brain-orbits.zip?downloa... | _____no_output_____ | Apache-2.0 | prototyping/auto-segmentation/sb/05-mini-data-take-2/072-score-with-scharr.ipynb | dg1an3/pymedphys |
Load Packages | import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['figure.figsize'] = [20, 10] | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Gathering Data Load Datasets, Toxicity & Aggression | filepath = "../../../Documents/"
aggression = filepath+"Aggression/"
toxicity = filepath+"Toxicity/"
toxic_annotated_comments = pd.read_csv(toxicity+"toxicity_annotated_comments.tsv", sep='\t')
toxic_annotations = pd.read_csv(toxicity+"toxicity_annotations.tsv", sep='\t')
toxic_demographics = pd.read_csv(toxicity+"toxi... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
What is inside the [Datasets](https://meta.wikimedia.org/wiki/Research:Detox/Data_Release)? | toxic_df = toxic_annotations.merge(toxic_annotated_comments, on= 'rev_id', how = 'left').merge(toxic_demographics, on = 'worker_id', how = 'left')
toxic_df.head()
#Some worker ids not included in toxic demographics
len(set(toxic_annotations.worker_id) - set(toxic_demographics.worker_id))
aggressions_df = agg_annotation... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Research Questions How has toxicity and aggression changed over time? Toxicity Toxicity is measured two ways. We can see the ratio of toxic comments vs non toxic comments over time, and the average toxicity score over time. Toxicity is a binary value column that we can do a count over number of rows to find the ra... | toxic_df.head()
plt.hist(toxic_df.toxicity_score)
plt.title("Toxicity Score Distribution") | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Toxicity Scores appear to have more positive or healthy comments than toxic comments Lets first look at a general time trend of the entire dataset on a per year basis Preprocessing | col_rename = {'rev_id':'count'}
toxic_df_trend = toxic_df.groupby('year').agg({'rev_id':'count', 'toxicity':'sum', 'toxicity_score':'mean'}).reset_index().rename(columns = col_rename)
toxic_df_trend['toxicity_ratio'] = toxic_df_trend['toxicity'] / toxic_df_trend['count']
toxic_df_trend['toxicity_score_reversed'] = -1 *... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Toxicity Ratio | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(toxic_df_trend.year, toxic_df_trend.toxicity_ratio)
plt.grid(True)
plt.title("Toxicity Ratio Over Time") | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Toxicity Score | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(toxic_df_trend.year, toxic_df_trend.toxicity_score_reversed)
plt.grid(True)
plt.title("Toxicity Score Over Time") | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Interestingly, there appears to be a rapid increase in both the average toxicity score and the ratio of toxic comments from the beginning of the dataset to around 2008. However, the rates appear to flat out remaining consistent in later years.My next question is, how do these results change for a user that was logged ... | col_rename = {'rev_id':'count'}
toxic_df_trend_logged_in = toxic_df[toxic_df.logged_in == True].groupby('year').agg({'rev_id':'count', 'toxicity':'sum', 'toxicity_score':'mean'}).reset_index().rename(columns = col_rename)
toxic_df_trend_logged_in['toxicity_ratio'] = toxic_df_trend_logged_in['toxicity'] / toxic_df_trend... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Toxicity Ratio Log In | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(toxic_df_trend.year, toxic_df_trend.toxicity_ratio, color = 'black', label ='Total')
plt.plot(toxic_df_trend_logged_in.year, toxic_df_trend_logged_in.toxicity_ratio, color = 'blue', label ='Logged In')
plt.plot(toxic_df_trend_not_logged_in.year, toxic_df_trend_n... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Toxicity Score Log In | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(toxic_df_trend.year, toxic_df_trend.toxicity_score_reversed, color = 'black', label ='Total')
plt.plot(toxic_df_trend_logged_in.year, toxic_df_trend_logged_in.toxicity_score_reversed, color = 'blue', label ='Logged In')
plt.plot(toxic_df_trend_not_logged_in.year... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
There appears to be a big difference in both the scoring and ratio when the user is logged in or not. On the years after 2005 for the not logged in group, the average (reversed) toxicity score turns positive, while remaining a ratio of less than 0.5, indicating that the comments are very high in toxicity for users not... |
col_rename = {'rev_id':'count'}
toxic_df_trend_blocked = toxic_df[toxic_df['sample'] == 'blocked'].groupby('year').agg({'rev_id':'count', 'toxicity':'sum', 'toxicity_score':'mean'}).reset_index().rename(columns = col_rename)
toxic_df_trend_blocked['toxicity_ratio'] = toxic_df_trend_blocked['toxicity'] / toxic_df_trend... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Toxicity Ratio Sampling | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(toxic_df_trend.year, toxic_df_trend.toxicity_ratio, color = 'black', label ='Total')
plt.plot(toxic_df_trend_random.year, toxic_df_trend_random.toxicity_ratio, color = 'blue', label ='Random')
plt.plot(toxic_df_trend_blocked.year, toxic_df_trend_blocked.toxicity... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Toxicity Score Sampling | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(toxic_df_trend.year, toxic_df_trend.toxicity_score_reversed, color = 'black', label ='Total')
plt.plot(toxic_df_trend_random.year, toxic_df_trend_random.toxicity_score_reversed, color = 'blue', label ='Random')
plt.plot(toxic_df_trend_blocked.year, toxic_df_tren... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
There appears to be a big difference in both the scoring and ratio when the sampling is done from blocked sources or when it is collected randomly. When sampled from blocked sources, the toxicity score turns positive right on its peak at 2008, but drops to negative values after 2009. When the sample is picked randoml... | aggressions_df.head() | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Lets first look at a general time trend of the entire dataset on a per year basis Preprocessing | col_rename = {'rev_id':'count'}
aggressions_df_trend = aggressions_df.groupby('year').agg({'rev_id':'count', 'aggression':'sum', 'aggression_score':'mean'}).reset_index().rename(columns = col_rename)
aggressions_df_trend['aggression_ratio'] = aggressions_df_trend['aggression'] / aggressions_df_trend['count']
aggression... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
While neutral comments are highly dominant in here, there appears to be slightly more aggressive comments than healthy comments. Aggression Ratio | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(aggressions_df_trend.year, aggressions_df_trend.aggression_ratio)
plt.grid(True)
plt.title("Aggression Ratio Over Time") | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Agression Score | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(aggressions_df_trend.year, aggressions_df_trend.aggression_score_reversed)
plt.grid(True)
plt.title("Aggression Score Over Time") | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
The overall trend here is similar to what we saw with toxicity. There appears to be a sharp increase until year 2008 at its peak, plateauing consistently afterwards. The scoress however, appear to reach positive values before its peak at 2005 and remaining positive. While most comments appear to be neutral, there ar... | col_rename = {'rev_id':'count'}
aggressions_df_trend_logged_in = aggressions_df[aggressions_df.logged_in == True].groupby('year').agg({'rev_id':'count', 'aggression':'sum', 'aggression_score':'mean'}).reset_index().rename(columns = col_rename)
aggressions_df_trend_logged_in['aggression_ratio'] = aggressions_df_trend_lo... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Aggression Ratio Log In | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(aggressions_df_trend.year, aggressions_df_trend.aggression_ratio, color = 'black', label ='Total')
plt.plot(aggressions_df_trend_logged_in.year, aggressions_df_trend_logged_in.aggression_ratio, color = 'blue', label ='Logged In')
plt.plot(aggressions_df_trend_no... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Aggressions dataset appears to not have any data on comments made without logging in after year 2002. Lets look at the difference of blocked vs randomly sampled sources Preprocessing | col_rename = {'rev_id':'count'}
aggressions_df_trend_blocked = aggressions_df[aggressions_df['sample'] == 'blocked'].groupby('year').agg({'rev_id':'count', 'aggression':'sum', 'aggression_score':'mean'}).reset_index().rename(columns = col_rename)
aggressions_df_trend_blocked['aggression_ratio'] = aggressions_df_trend_b... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Aggression Ratio Sampling | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(aggressions_df_trend.year, aggressions_df_trend.aggression_ratio, color = 'black', label ='Total')
plt.plot(aggressions_df_trend_random.year, aggressions_df_trend_random.aggression_ratio, color = 'blue', label ='Random')
plt.plot(aggressions_df_trend_blocked.yea... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Aggression Score Sampling | plt.figure()
plt.ticklabel_format(style='plain')
plt.plot(aggressions_df_trend.year, aggressions_df_trend.aggression_score_reversed, color = 'black', label ='Total')
plt.plot(aggressions_df_trend_random.year, aggressions_df_trend_random.aggression_score_reversed, color = 'blue', label ='Random')
plt.plot(aggressions_df... | _____no_output_____ | MIT | data-512-a2/notebook.ipynb | jameslee0920/data-512 |
Solving `CartPole` Your task:Solve the `CartPole` environment. Which algorithms could you use? As a warm-up, implement first SARSA or Q-Learning in `FrozenLake`. Some starter code is below. Note that if you want to use these algorithms for `CartPole` you need to discretize the state space somehow. | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import gym
import math | _____no_output_____ | MIT | .ipynb_checkpoints/Exercise - Solving Cart-Pole-checkpoint.ipynb | jpmaldonado/packt-rl |
How could you know how to discretize?You can try to sample some elements from the observation space (=state space). Then discretize based on that. | cp_env = gym.make('CartPole-v0')
cp_obs = [cp_env.observation_space.sample() for _ in range(10000)]
plt.hist([ob[0] for ob in cp_obs] )
plt.title("Observation x")
plt.hist([ob[1] for ob in cp_obs] )
plt.title("Observation x_dot")
plt.hist([ob[2] for ob in cp_obs] )
plt.title("Observation theta")
plt.hist([ob[3] for ob ... | _____no_output_____ | MIT | .ipynb_checkpoints/Exercise - Solving Cart-Pole-checkpoint.ipynb | jpmaldonado/packt-rl |
Then, we define some limit for the borders. | STATE_BOUNDS = list(zip(cp_env.observation_space.low, cp_env.observation_space.high))
STATE_BOUNDS[1] = [-0.5, 0.5]
STATE_BOUNDS[3] = [-math.radians(50), math.radians(50)]
NUM_BUCKETS = (3,3,3,3) # state:n_bins pairs
def obs_to_state(obs):
bucket_indice = []
for i in range(len(obs)):
if obs[i] <= STATE_... | [33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.[0m
Number of episodes: 100 . Average 100-episode reward: 26.05
| MIT | .ipynb_checkpoints/Exercise - Solving Cart-Pole-checkpoint.ipynb | jpmaldonado/packt-rl |
Installation - Run these commands - git clone https://github.com/Tessellate-Imaging/Monk_Object_Detection.git - cd Monk_Object_Detection/6_cornernet_lite/installation - Select the right requirements file and run - chmod +x install.sh - ./install.sh About the network1. Paper on CornerNe... | import os
import sys
sys.path.append("../../6_cornernet_lite/lib/")
from train_detector import Detector
gtf = Detector();
root_dir = "../sample_dataset";
coco_dir = "kangaroo"
img_dir = "/"
set_dir = "Images"
gtf.Train_Dataset(root_dir, coco_dir, img_dir, set_dir, batch_size=4, use_gpu=True, num_workers=4)
gtf.Model(mo... | start_iter = 0
distributed = False
world_size = 0
initialize = False
batch_size = 4
learning_rate = 0.00025
max_iteration = 1000
stepsize = 800
snapshot = 500
val_iter = 500
display = 100
decay_rate = 10
Process 0: building model...
total paramet... | Apache-2.0 | example_notebooks/6_cornernet_lite/Train CornerNet-Squeeze.ipynb | jayeshk7/Monk_Object_Detection |
Inference | import os
import sys
sys.path.append("../../6_cornernet_lite/lib/")
from infer_detector import Infer
gtf = Infer();
class_list = ["kangaroo"]
gtf.Model(class_list,
base="CornerNet_Squeeze",
model_path="./cache/nnet/CornerNet_Squeeze/CornerNet_Squeeze_final.pkl")
boxes = gtf.Predict("../sample_data... | _____no_output_____ | Apache-2.0 | example_notebooks/6_cornernet_lite/Train CornerNet-Squeeze.ipynb | jayeshk7/Monk_Object_Detection |
Dataframes: The Basics This tutorial will cover the following topics:* Storing a dataframe as a TileDB 1D dense array to allow fast (out-of-core) slicing on rows* Storing a dataframe as a TileDB ND sparse array to allow fast (out-of-core) execution of column range predicates* Interoperating with Pandas and [Apache Arr... | !wget https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2020-01.csv
!ls -alh yellow_tripdata_2020-01.csv | -rw-rw---- 1 stavros staff 566M Jul 30 00:07 yellow_tripdata_2020-01.csv
| MIT | tutorials/notebooks/python/dataframes/df_basics.ipynb | TileDB-Inc/TileDB-Examples |
InstallationYou need to install [TileDB-Py](https://github.com/TileDB-Inc/TileDB-Py), the Python wrapper of [TileDB Embedded](https://github.com/TileDB-Inc/TileDB), as follows: ```bash Pip:$ pip install tiledb Or Conda:$ conda install -c conda-forge tiledb-py``` The notebook was run using **Pandas 1.1.0**. Note that t... | import tiledb, numpy as np
# Version of TileDB core (C++ library)
tiledb.libtiledb.version()
# Version of TileDB-Py (Python wrapper)
tiledb.__version__ | _____no_output_____ | MIT | tutorials/notebooks/python/dataframes/df_basics.ipynb | TileDB-Inc/TileDB-Examples |
Before we start, we create the TileDB context passing a **configuration parameter** around memory allocation during read queries that will be explained in a later tutorial. That needs to be set at the *very beginning* of the code and before any other TileDB function is called. | cfg = tiledb.Ctx().config()
cfg.update(
{
'py.init_buffer_bytes': 1024**2 * 50
}
)
tiledb.default_ctx(cfg) | _____no_output_____ | MIT | tutorials/notebooks/python/dataframes/df_basics.ipynb | TileDB-Inc/TileDB-Examples |
We also enable the TileDB **stats** so that we can get some insight into performance. | tiledb.stats_enable() | _____no_output_____ | MIT | tutorials/notebooks/python/dataframes/df_basics.ipynb | TileDB-Inc/TileDB-Examples |
The Dense Case We ingest the `yellow_tripdata_2020-01.csv` CSV file into a TileDB dense array as shown below. The command takes the taxi CSV file and ingests it into a 1D dense array called `taxi_dense_array`. It sets the tile extent to 100K, which means that groups of 100K rows each across every column will comprise ... | %%time
tiledb.stats_reset()
tiledb.from_csv("taxi_dense_array", "yellow_tripdata_2020-01.csv",
tile = 100000,
parse_dates=['tpep_dropoff_datetime', 'tpep_pickup_datetime'],
fillna={'store_and_fwd_flag': ''})
tiledb.stats_dump() | /opt/miniconda3/envs/tiledb/lib/python3.8/site-packages/IPython/core/magic.py:187: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.
call = lambda f, *a, **k: f(*a, **k)
| MIT | tutorials/notebooks/python/dataframes/df_basics.ipynb | TileDB-Inc/TileDB-Examples |
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