markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
values |
|---|---|---|---|---|
Saving/loading sessions
Saving and loading Symca sessions is very simple and works similar to RateChar. Saving a session takes place with the save_session method, whereas the load_session method loads the saved expressions. As with the save_results method and most other saving and loading functionality, if no file_name... | # saving session
sc.save_session()
# create new Symca object and load saved results
new_sc = psctb.Symca(mod)
new_sc.load_session()
# display saved results
new_sc.cc_results | example_notebooks/Symca.ipynb | PySCeS/PyscesToolbox | bsd-3-clause |
Plotting Support | %matplotlib inline
import matplotlib.pyplot as plt | examples/nonmarkov-transfer-tensor-method.ipynb | qutip/qutip-notebooks | lgpl-3.0 |
Jaynes-Cummings model, with the cavity as a non-Markovian bath
As a simple example, we consider the Jaynes-Cummings mode, and the non-Markovian dynamics of the qubit when the cavity is traced out. In this example, the dynamical maps $\mathcal{E}_k$ are the reduced time-propagators for the qubit, after evolving and trac... | kappa = 1.0 # cavity decay rate
wc = 0.0*kappa # cavity frequency
wa = 0.0*kappa # qubit frequency
g = 10.0*kappa # coupling strength
N = 3 # size of cavity basis
# intial state
psi0c = qt.basis(N,0)
rho0c = qt.ket2dm(psi0c)
rho0a = qt.ket2dm(qt.basis(2,0))
rho0 = qt.tensor(rho0a,rho0c)
rho0avec = qt.operator_to_vecto... | examples/nonmarkov-transfer-tensor-method.ipynb | qutip/qutip-notebooks | lgpl-3.0 |
Exact timepropagators to learn from
The function dynmap generates an exact timepropagator for the qubit $\mathcal{E}_{k}$ for a time $t_k$. <br> | def dynmap(t):
# reduced dynamical map for the qubit at time t
Et = qt.mesolve(H, E0, [0.,t], c_ops, []).states[-1]
return ptracesuper*(Et*superrho0cav) | examples/nonmarkov-transfer-tensor-method.ipynb | qutip/qutip-notebooks | lgpl-3.0 |
Exact time evolution using standard mesolve method | exacttimes = np.arange(0,5,0.01)
exactsol = qt.mesolve(H, rho0, exacttimes, c_ops, []) | examples/nonmarkov-transfer-tensor-method.ipynb | qutip/qutip-notebooks | lgpl-3.0 |
Approximate solution using the Transfer Tensor Method for different learning times | times = np.arange(0,5,0.1) # total extrapolation time
ttmsols = []
maxlearningtimes = [0.5, 2.0] # maximal learning times
for T in maxlearningtimes:
learningtimes = np.arange(0,T,0.1)
learningmaps = [dynmap(t) for t in learningtimes] # generate exact dynamical maps to learn from
ttmsols.append(ttm.ttmsolve(... | examples/nonmarkov-transfer-tensor-method.ipynb | qutip/qutip-notebooks | lgpl-3.0 |
Visualize results | fig, ax = plt.subplots(figsize=(10,7))
ax.plot(exactsol.times, qt.expect(sz, exactsol.states),'-b',linewidth=3.0)
style = ['og','or']
for i,ttmsol in enumerate(ttmsols):
ax.plot(ttmsol.times, qt.expect(qt.sigmaz(), ttmsol.states),style[i],linewidth=1.5,)
ax.legend(['exact',str(maxlearningtimes[0]),str(maxlearningti... | examples/nonmarkov-transfer-tensor-method.ipynb | qutip/qutip-notebooks | lgpl-3.0 |
Discussion
The figure above illustrates how the transfer tensor method needs a sufficiently long set of learning times to get good results. The green dots show results for learning times $t_k=0,0.1,\dots,0.5$, which is clearly not sufficient. The red dots show results for $t_k=0,0.1,\dots,2.0$, which gives results that... | version_table() | examples/nonmarkov-transfer-tensor-method.ipynb | qutip/qutip-notebooks | lgpl-3.0 |
Como podemos ver, utilizando simples expresiones de Python, podemos cargar la base de datos de la ONG en un Dataframe de Pandas; lo que nos va a permitir manipular los datos con suma facilidad. Comenzemos a explorar un poco más en detalle este dataset!
En primer lugar, lo que deberíamos hacer es controlar si existen va... | # Controlando valores nulos
ONG_data.isnull().any().any() | content/notebooks/MachineLearningPractica.ipynb | relopezbriega/mi-python-blog | gpl-2.0 |
Como podemos ver, el método nos devuelve el valor "True", lo que indica que existen valores nulos en nuestro dataset. Estos valores pueden tener una influencia significativa en nuestro modelo predictivo, por lo que siempre es una decisión importante determinar la forma en que los vamos a manejar. Las alternativas que t... | # Agrupando columnas por tipo de datos
tipos = ONG_data.columns.to_series().groupby(ONG_data.dtypes).groups
# Armando lista de columnas categóricas
ctext = tipos[np.dtype('object')]
len(ctext) # cantidad de columnas con datos categóricos.
# Armando lista de columnas numéricas
columnas = ONG_data.columns # lista de... | content/notebooks/MachineLearningPractica.ipynb | relopezbriega/mi-python-blog | gpl-2.0 |
Ahora ya logramos separar a las 481 columnas que tiene nuestro dataset. 68 columnas contienen datos categóricos y 413 contienen datos cuantitativos. Procedamos a inferir los valores faltantes. | # Completando valores faltantas datos cuantititavos
for c in cnum:
mean = ONG_data[c].mean()
ONG_data[c] = ONG_data[c].fillna(mean)
# Completando valores faltantas datos categóricos
for c in ctext:
mode = ONG_data[c].mode()[0]
ONG_data[c] = ONG_data[c].fillna(mode)
# Controlando que no hayan valores f... | content/notebooks/MachineLearningPractica.ipynb | relopezbriega/mi-python-blog | gpl-2.0 |
Perfecto! Ahora tenemos un dataset limpio de valores faltantes. Ya estamos listos para comenzar a explorar los datos, comencemos por determinar el porcentaje de personas que alguna vez fue donante de la ONG y están incluidos en la base de datos con la que estamos trabajando. | # Calculando el porcentaje de donantes sobre toda la base de datos
porcent_donantes = (ONG_data[ONG_data.DONOR_AMOUNT
> 0]['DONOR_AMOUNT'].count() * 1.0
/ ONG_data['DONOR_AMOUNT'].count()) * 100.0
print("El procentaje de donantes de la base de datos es {0:.2f}%"
.format(porcen... | content/notebooks/MachineLearningPractica.ipynb | relopezbriega/mi-python-blog | gpl-2.0 |
Aquí podemos ver que el porcentaje de personas que fueron donantes en el pasado es realmente muy bajo, solo un 5 % del total de la base de datos (2423 personas). Este es un dato importante a tener en cuenta ya que al existir tanta diferencia entre las clases a clasificar, esto puede afectar considerablemente a nuestro ... | # Analizando el importe de donanciones
# Creando un segmentos de importes
imp_segm = pd.cut(ONG_donantes['DONOR_AMOUNT'],
[0, 10, 20, 30, 40, 50, 60, 100, 200])
# Creando el grafico de barras desde pandas
plot = pd.value_counts(imp_segm).plot(kind='bar',
title='... | content/notebooks/MachineLearningPractica.ipynb | relopezbriega/mi-python-blog | gpl-2.0 |
Este análisis nos muestra que la mayor cantidad de donaciones caen en un rango de importes entre 0 y 30, siendo la donación promedio 15.60. También podemos ver que donaciones que superen un importe de 50 son casos realmente poco frecuentes, por lo que constituyen valores atípicos y sería prudente eliminar estos casos a... | # Grafico del género de los donantes
ONG_donantes.groupby('GENDER').size().plot(kind='bar')
plt.title('Distribución por género')
plt.show()
# Donaciones segun el género
ONG_donantes[(ONG_donantes.DONOR_AMOUNT <= 50)
& (ONG_donantes.GENDER.isin(['F', 'M'])
)][['DONOR_AMOUNT', 'GENDER']].boxp... | content/notebooks/MachineLearningPractica.ipynb | relopezbriega/mi-python-blog | gpl-2.0 |
Aquí vemos que las mujeres suelen estar más propensas a donar, aunque donan un importe promedio menor (14.61) al que donan los hombres (16.82). Veamos ahora como se comportan las donaciones respecto a la edad. | # Distribución de la edad de los donantes
ONG_donantes['AGE'].hist().set_title('Distribución de donantes segun edad')
plt.show()
# Agrupando la edad por rango de a 10
AGE2 = pd.cut(ONG_donantes['AGE'], range(0, 100, 10))
ONG_donantes['AGE2'] = AGE2
# Gráfico de barras de donaciones por edad
pd.value_counts(AGE2).plot... | content/notebooks/MachineLearningPractica.ipynb | relopezbriega/mi-python-blog | gpl-2.0 |
Question 2
Which one of the following modules is not part of the Bio.Blast package in Biopython:
ParseBlastTable
NCBIXML
FastaIO
Applications | import Bio.Blast
help(Bio.Blast) | Python for Genomic Data Science/Lecture 8 Quiz.ipynb | ysh329/Homework | mit |
Question 3
Using Biopython find out what species the following unknown DNA sequence comes from:
TGGGCCTCATATTTATCCTATATACCATGTTCGTATGGTGGCGCGATGTTCTACGTGAATCCACGTTCGAAGGACATCATACCAAAGTCGTAC
AATTAGGACCTCGATATGGTTTTATTCTGTTTATCGTATCGGAGGTTATGTTCTTTTTTGCTCTTTTTCGGGCTTCTTCTCATTCTTCTTTGGCAC
CTACGGTAGAG
Hint. Identify the al... | from Bio.Blast import NCBIWWW
fasta_string = "TGGGCCTCATATTTATCCTATATACCATGTTCGTATGGTGGCGCGATGTTCTACGTGAATCCACGTTCGAAGGACATCATACCAAAGTCGTACAATTAGGACCTCGATATGGTTTTATTCTGTTTATCGTATCGGAGGTTATGTTCTTTTTTGCTCTTTTTCGGGCTTCTTCTCATTCTTCTTTGGCACCTACGGTAGAG"
result_handle = NCBIWWW.qblast("blastn", "nt", fasta_string)
from Bio.B... | Python for Genomic Data Science/Lecture 8 Quiz.ipynb | ysh329/Homework | mit |
Question 4
Seq is a sequence object that can be imported from Biopython using the following statement:
'''
from Bio.Seq import Seq
'''
If my_seq is a Seq object, what is the correct Biopython code to print the reverse complement of my_seq?
Hint. Use the built-in function help you find out the methods of the Seq object... | from Bio.Seq import Seq
from Bio.Alphabet import generic_protein
help(Seq)
my_seq = Seq("MELKI", generic_protein) + "LV"
help(my_seq)
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
my_dna = Seq("CCCCCGATAG", IUPAC.unambiguous_dna)
my_dna
my_dna.complement()
from Bio.Seq import Seq
from Bio.Alphabet import I... | Python for Genomic Data Science/Lecture 8 Quiz.ipynb | ysh329/Homework | mit |
Question 5
Create a Biopython Seq object that represents the following sequence:
TGGGCCTCATATTTATCCTATATACCATGTTCGTATGGTGGCGCGATGTTCTACGTGAATCCACGTTCGAAGGACATCATACCAAAGTCGTAC
AATTAGGACCTCGATATGGTTTTATTCTGTTTATCGTATCGGAGGTTATGTTCTTTTTTGCTCTTTTTCGGGCTTCTTCTCATTCTTCTTTGGCAC
CTACGGTAGAG
Its protein translation is:
ILASY... | from Bio.Blast import NCBIWWW
my_seq = Seq("TGGGCCTCATATTTATCCTATATACCATGTTCGTATGGTGGCGCGATGTTCTACGTGAATCCACGTTCGAAGGACATCATACCAAAGTCGTACAATTAGGACCTCGATATGGTTTTATTCTGTTTATCGTATCGGAGGTTATGTTCTTTTTTGCTCTTTTTCGGGCTTCTTCTCATTCTTCTTTGGCACCTACGGTAGAG")
print my_seq.translate() | Python for Genomic Data Science/Lecture 8 Quiz.ipynb | ysh329/Homework | mit |
Second Step: Visualizing Prediction | # ignore this, it is just technical code
# should come from a lib, consider it to appear magically
# http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
cmap_print = ListedColormap(['#AA8888', '#004000', '#FFFFDD... | notebooks/ml/1-classic-code.ipynb | DJCordhose/ai | mit |
By just randomly guessing, we get approx. 1/3 right, which is what we expect | random_clf.score(X, y) | notebooks/ml/1-classic-code.ipynb | DJCordhose/ai | mit |
Third Step: Creating a Base Line
Creating a naive classifier manually, how much better is it? | class BaseLineClassifier(ClassifierBase):
def predict_single(self, x):
try:
speed, age, km_per_year = x
except:
speed, age = x
km_per_year = 0
if age < 25:
if speed > 180:
return 0
else:
return 2
... | notebooks/ml/1-classic-code.ipynb | DJCordhose/ai | mit |
This is the baseline we have to beat | base_clf.score(X, y) | notebooks/ml/1-classic-code.ipynb | DJCordhose/ai | mit |
Enter Team Member Names here (double click to edit):
Name 1:
Name 2:
Name 3:
In Class Assignment Two
In the following assignment you will be asked to fill in python code and derivations for a number of different problems. Please read all instructions carefully and turn in the rendered notebook (or HTML of the render... | # fetch the images for the dataset
# this will take a long time the first run because it needs to download
# after the first time, the dataset will be save to your disk (in sklearn package somewhere)
# if this does not run, you may need additional libraries installed on your system (install at your own risk!!)
from sk... | In_Class/ICA2_MachineLearning_PartA.ipynb | Tsiems/machine-learning-projects | mit |
Question 1: For the faces dataset, describe what the data represents? That is, what is each column? What is each row? What do the unique class values represent?
Every column is a pixel location in a 125x94 photograph.
Each row is a single image of someone's face.
The unique class values are the names of the people in t... | # Enter any scratchwork or calculations here
| In_Class/ICA2_MachineLearning_PartA.ipynb | Tsiems/machine-learning-projects | mit |
Question 3:
- Part A: Given the number of parameters calculated above, would you expect the model to train quickly using batch optimization techniques? Why or why not?
- Part B: Is there a way to reduce training time?
- Part C: If we transformed the X data using principle components analysis (PCA) with 100 components... | # Enter any scratchwork or calculations here
print('Part C. With 100 features: ', '100') | In_Class/ICA2_MachineLearning_PartA.ipynb | Tsiems/machine-learning-projects | mit |
Signal Processing
Scope
Signal processing today:
Communication,
Sensors,
Images,
Video,
...
Analog vs. Digital signals
Analog signal
Analog signal is a continuous function of time:
$$
x : t \mapsto x(t)
$$
Digital signal
Digital signal is a discrete function of time:
$$
t_n = t_0 + n \times \delta t
$$
$$
x_n = x(t... |
import numpy as np
import matplotlib.pyplot as plt
def signal(t, f = 1.):
return np.cos(2. * np.pi * f * t)
D = 3.2 # duration
t = np.linspace(0., D, 1000)
x = signal(t)
fs = 10. # sampling rate
tn = np.linspace(0., D, fs * D)
xn = signal(tn)
plt.plot(t, x, "k-", label = "Analog")
plt.plot(tn, xn, "bo--", l... | doc/Traitement_signal/signal_processing.ipynb | lcharleux/numerical_analysis | gpl-2.0 |
Digital signals
Effect of the sampling rate | t = np.linspace(0., D, 1000)
x = signal(t)
Fs = [1., 2., 10.] # sampling rate
plt.plot(t, x, "k-", label = "Analog")
for fs in Fs:
tn = np.linspace(0., D, fs * D)
xn = signal(tn)
plt.plot(tn, xn, "o--", label = "fs = {0}".format(fs))
plt.grid()
plt.xlabel("Time, $t$")
plt.ylabel("Amplitude, $x$")
plt.legend(... | doc/Traitement_signal/signal_processing.ipynb | lcharleux/numerical_analysis | gpl-2.0 |
Higher sampling rate means better signal description,
Lower sampling rate means loss of information,
Aliasing |
D = 1.5 # duration
t = np.linspace(0., D, 1000)
fs = 2.5 # sampling rate
tn = np.linspace(0., D, fs * D)
xn = signal(tn)
F = .5 + np.arange(3) * fs # sampling rate
tn = np.linspace(0., D, fs * D)
xn = signal(tn, f = F[0])
plt.plot(tn, xn, "ok", label = "Samples")
for f in F:
x = signal(t, f = f) ... | doc/Traitement_signal/signal_processing.ipynb | lcharleux/numerical_analysis | gpl-2.0 |
BERT Question Answer with TensorFlow Lite Model Maker
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://www.tensorflow.org/lite/models/modify/model_maker/question_answer"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
</td>
<td>... | !sudo apt -y install libportaudio2
!pip install -q tflite-model-maker-nightly | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
Import the required packages. | import numpy as np
import os
import tensorflow as tf
assert tf.__version__.startswith('2')
from tflite_model_maker import model_spec
from tflite_model_maker import question_answer
from tflite_model_maker.config import ExportFormat
from tflite_model_maker.question_answer import DataLoader | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
The "End-to-End Overview" demonstrates a simple end-to-end example. The following sections walk through the example step by step to show more detail.
Choose a model_spec that represents a model for question answer
Each model_spec object represents a specific model for question answer. The Model Maker currently supports... | spec = model_spec.get('mobilebert_qa_squad') | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
Load Input Data Specific to an On-device ML App and Preprocess the Data
The TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. In this tutorial, you will use a subset of this dataset to learn how to use the Model Maker library.
To load the data, convert the TriviaQA datas... | train_data_path = tf.keras.utils.get_file(
fname='triviaqa-web-train-8000.json',
origin='https://storage.googleapis.com/download.tensorflow.org/models/tflite/dataset/triviaqa-web-train-8000.json')
validation_data_path = tf.keras.utils.get_file(
fname='triviaqa-verified-web-dev.json',
origin='https://sto... | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
You can also train the MobileBERT model with your own dataset. If you are running this notebook on Colab, upload your data by using the left sidebar.
<img src="https://storage.googleapis.com/download.tensorflow.org/models/tflite/screenshots/model_maker_question_answer.png" alt="Upload File" width="800" hspace="100">
If... | train_data = DataLoader.from_squad(train_data_path, spec, is_training=True)
validation_data = DataLoader.from_squad(validation_data_path, spec, is_training=False) | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
Customize the TensorFlow Model
Create a custom question answer model based on the loaded data. The create function comprises the following steps:
Creates the model for question answer according to model_spec.
Train the question answer model. The default epochs and the default batch size are set according to two variab... | model = question_answer.create(train_data, model_spec=spec) | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
Have a look at the detailed model structure. | model.summary() | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
Evaluate the Customized Model
Evaluate the model on the validation data and get a dict of metrics including f1 score and exact match etc. Note that metrics are different for SQuAD1.1 and SQuAD2.0. | model.evaluate(validation_data) | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
Export to TensorFlow Lite Model
Convert the trained model to TensorFlow Lite model format with metadata so that you can later use in an on-device ML application. The vocab file are embedded in metadata. The default TFLite filename is model.tflite.
In many on-device ML application, the model size is an important factor.... | model.export(export_dir='.') | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
You can use the TensorFlow Lite model file in the bert_qa reference app using BertQuestionAnswerer API in TensorFlow Lite Task Library by downloading it from the left sidebar on Colab.
The allowed export formats can be one or a list of the following:
ExportFormat.TFLITE
ExportFormat.VOCAB
ExportFormat.SAVED_MODEL
By ... | model.export(export_dir='.', export_format=ExportFormat.VOCAB) | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
You can also evaluate the tflite model with the evaluate_tflite method. This step is expected to take a long time. | model.evaluate_tflite('model.tflite', validation_data) | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
Advanced Usage
The create function is the critical part of this library in which the model_spec parameter defines the model specification. The BertQASpec class is currently supported. There are 2 models: MobileBERT model, BERT-Base model. The create function comprises the following steps:
Creates the model for questio... | new_spec = model_spec.get('mobilebert_qa')
new_spec.seq_len = 512 | site/en-snapshot/lite/models/modify/model_maker/question_answer.ipynb | tensorflow/docs-l10n | apache-2.0 |
Step 1- split
Given a list let's split it into two lists right down the middle | def split(input_list):
"""
Splits a list into two pieces
:param input_list: list
:return: left and right lists (list, list)
"""
input_list_len = len(input_list)
midpoint = input_list_len // 2
return input_list[:midpoint], input_list[midpoint:]
tests_split = [
({'input_list': [1, 2, ... | algorithms/Merge-Sort.ipynb | amirziai/learning | mit |
Step 2- merge sorted lists
Given two sorted lists we should be able to "merge" them into a single list as a linear operation | def merge_sorted_lists(list_left, list_right):
"""
Merge two sorted lists
This is a linear operation
O(len(list_right) + len(list_right))
:param left_list: list
:param right_list: list
:return merged list
"""
# Special case: one or both of lists are empty
if len(list_left) == 0:
... | algorithms/Merge-Sort.ipynb | amirziai/learning | mit |
Step 3- merge sort
Merge sort only needs to utilize the previous 2 functions
We need to split the lists until they have a single element
A list with a single element is sorted (duh)
Now we can merge these single-element (or empty) lists | def merge_sort(input_list):
if len(input_list) <= 1:
return input_list
else:
left, right = split(input_list)
# The following line is the most important piece in this whole thing
return merge_sorted_lists(merge_sort(left), merge_sort(right))
random_list = [random.randint(1, 1000)... | algorithms/Merge-Sort.ipynb | amirziai/learning | mit |
Above: Fit looks pretty wild, too. Here's a clear example of how overfitting is associated with very large magnitude estimated coefficients.
Ridge Regression
Ridge regression aims to avoid overfitting by adding a cost to the RSS term of standard least squares that depends on the 2-norm of the coefficients $\|w\|$. Th... | def polynomial_ridge_regression(data, deg, l2_penalty):
model = graphlab.linear_regression.create(polynomial_features(data,deg),
target='Y', l2_penalty=l2_penalty,
validation_set=None,verbose=False)
return model | Overfitting_Ridge_Lasso.ipynb | anilcs13m/MachineLearning_Mastering | gpl-2.0 |
Let's look at fits for a sequence of increasing lambda values | for l2_penalty in [1e-25, 1e-10, 1e-6, 1e-3, 1e2]:
model = polynomial_ridge_regression(data, deg=16, l2_penalty=l2_penalty)
print 'lambda = %.2e' % l2_penalty
print_coefficients(model)
print '\n'
plt.figure()
plot_poly_predictions(data,model)
plt.title('Ridge, lambda = %.2e' % l2_penalty) | Overfitting_Ridge_Lasso.ipynb | anilcs13m/MachineLearning_Mastering | gpl-2.0 |
We can take a look at the first 15 rows of the table: | t[:15] | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
The model name is a unique name that identifies each model and the viewing angle is indicated in the suffix (e.g. _01). The value of the inclination is also given in the inclination column. The remaining columns give the parameters for the models (which columns are present depends on the model set). The scattering colu... | from sedfitter.sed import SEDCube
seds = SEDCube.read('sp--s-i/flux.fits') | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
This 'SED cube' is an efficient way to store the models fluxes in a single 3D array, where the three dimensions are the model, the aperture, and the wavelength.
The model names can be accessed with: | print(seds.names) | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
while the apertures, wavelengths, and frequencies can be accessed with: | print(seds.apertures)
print(seds.wav)
print(seds.nu) | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
A valid flag is used to indicate models that do not have complete/valid SEDs (for example because the model run did not complete): | print(seds.valid) | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
The fluxes and errors can be obtained using the val and
error attributes. We can check the shape of these arrays to check that they are indeed 3D arrays: | seds.val.shape
seds.val.shape | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
For this model set, there are 90000 models (10000 physical models times 9 inclinations), 20 apertures, and 200 wavelengths.
To access a specific SED, you can call seds.get_sed using a particular
model name: | sed = seds.get_sed('00p13Elr_03') | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
The wavelength, flux, and error can then be accessed with: | print(sed.wav)
print(sed.flux)
print(sed.error) | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
The SED is a 2D array with dimensions the number of apertures (20) and the number of wavelengths (200): | sed.flux.shape | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
We can use this to visualize the SED: | %matplotlib inline
import matplotlib.pyplot as plt
_ = plt.loglog(sed.wav, sed.flux.transpose(), 'k-', alpha=0.5)
_ = plt.ylim(1e-2, 1e8) | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
Fitting SEDs to data
To fit SEDs to observed data, you can also make use of the sedfitter package. What follows is a very short example - for more information on using the sedfitter package, be sure to read over the documentation.
To demonstrate this, we will fit the above models to the data for the NGC2264 source mod... | %cat data_ngc2264_20 | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
We start off by setting up the list of filters/wavelengths and approximate aperture radii used: | from astropy import units as u
filters = ['BU', 'BB', 'BV', 'BR', 'BI', '2J', '2H', '2K', 'I1', 'I2',
5.580 * u.micron, 7.650 * u.micron, 9.95 * u.micron,
12.93 * u.micron, 17.72 * u.micron, 24.28 * u.micron,
29.95 * u.micron, 35.06 * u.micron,
'M2', 'M3', 'W1', 'W2']
apert... | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
We also set up the extinction law used in Robitaille (2017): | from sedfitter.extinction import Extinction
extinction = Extinction.from_file('whitney.r550.par') | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
Finally, we run the fitting: | import sedfitter
sedfitter.fit('data_ngc2264_20', filters, apertures, 'sp--s-i',
'output_ngc2264_sp--s-i.fitinfo',
extinction_law=extinction,
distance_range=[0.869, 0.961] * u.kpc,
av_range=[0., 40.],
output_format=('F', 3.),
output_co... | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
We now generate the SED plots with the data to examine the fit: | sedfitter.plot('output_ngc2264_sp--s-i.fitinfo',
output_dir='plots_sed', format='png',
plot_mode='A',
select_format=('F', 3.),
show_convolved=False, show_sed=True,
x_mode='M', x_range=(0.1, 2000),
y_mode='M', y_range=(1.e-14, 2e-8... | notebook/using_the_models.ipynb | hyperion-rt/paper-2017-sed-models | bsd-2-clause |
Dataset | def ground_truth(x):
return x * np.sin(x) + np.sin(2 * x)
def gen_data(n_samples=200):
np.random.seed(13)
x = np.random.uniform(0, 10, size=n_samples)
x.sort()
y = ground_truth(x) + 0.75 * np.random.normal(size=n_samples)
train_mask = np.random.randint(0, 2, size=n_samples).astype(np.bool)
... | notebooks/s2-1/Ensembles.ipynb | amorgun/shad-ml-notebooks | unlicense |
RF | n_estimators = 1000
rf = RandomForestRegressor(n_estimators=n_estimators, random_state=30)
loss = metrics.mean_squared_error
rf.fit(X_train, y_train)
rf_errors = []
rf_estimators = rf.estimators_
for n in range(1, n_estimators):
rf.estimators_ = rf_estimators[:n]
rf_errors.append(loss(y_test, rf.predict(X_test)... | notebooks/s2-1/Ensembles.ipynb | amorgun/shad-ml-notebooks | unlicense |
GBT
$$\tilde{x}^m = \tilde{x}^{m-1} - \lambda_m \nabla f(\tilde{x}^{m-1})$$
$$\tilde{y}^m = \tilde{y}^{m-1} - \lambda_m \nabla Q(\tilde{y}^{m-1}, y)$$
$$b_i = learn(X, -\nabla Q(\tilde{y}^{m-1}, y))$$
Example
$$ Q(\tilde{y}^m, y) = \frac12 \sum_{i=1}^L (\tilde{y}i^m - y_i)^2 $$
$$ -\nabla Q(\tilde{y}^m, y)
= -\nabla ... | def get_ensemble_errors(clf):
clf.fit(X_train, y_train)
train_loss , test_loss= [], []
estimators = clf.estimators_
for n in range(1, n_estimators):
clf.estimators_ = estimators[:n]
train_loss.append(loss(y_train, clf.predict(X_train)))
test_loss.append(loss(y_test, clf.predict(X... | notebooks/s2-1/Ensembles.ipynb | amorgun/shad-ml-notebooks | unlicense |
1. 随机变量 Random Variable
定义：设随机试验的样本空间为 S = {e}。X = X(e)是定义在样本空间S上的实值单值函数。称 X = X(e)为随机变量。
例：将一枚硬币抛掷三次，观察出现正面和反面的情况，样本空间是
S = {HHH, HHT, HTH, THH, HTT, THT, TTH, TTT}。
以X记三次投掷得到正面H的总数，那么，对于样本空间 S = {e}（用 e 代表样本空间的元素，而将样本空间记成{e}）中的每一个样本点 e，X 都有一个数与之对应。X 是定义在样本空间 S 上的一个实值单值函数。它的定义域是样本空间 S，值域是实数集合{0, 1, 2, 3}。使用函数记号可将X写成
$... | # 投掷硬币10次，正面朝上的次数；重复100次
n, p = 10, .5
np.random.binomial(n, p, 100) | Random Variable and its Distribution.ipynb | reata/ProbabilityAndStatistics | mit |
一个现实生活中的例子。一家钻井公司探索九个矿井，预计每个开采成功率为0.1；九个矿井全部开采失败的概率是多少？
根据公式，$n = 9, p = 0.1, P{X = 0} = \binom{9}{0} \cdot 0.1^{0} \cdot 0.9^{9} \approx 0.3874$
我们对该模型进行20000次试验，计算其中得到0的概率： | sum(np.random.binomial(9, 0.1, 20000) == 0) / 20000 | Random Variable and its Distribution.ipynb | reata/ProbabilityAndStatistics | mit |
将试验次数增加，可以模拟出更加逼近准确值的结果。
4. 泊松分布 Poisson Distribution
设随机变量 X 所有可能取的值为0, 1, 2, ..., 而取各个值的概率为
$$P{X=k} = \frac{\lambda^ke^{-\lambda}}{k!}, k=0,1,2,...,$$
其中 $\lambda > 0$ 是常数，则称 $X$ 服从参数为 $\lambda$ 的泊松分布，记为 $X \sim \pi(\lambda)$。
易知，$P{X=k}\geq0,k=0,1,2,...$，且有
$$ \sum_{k=0}^\infty P{X=k} = \sum_{k=0}^\infty \frac{\lam... | lb = 5
s = np.random.poisson(lb, 10000)
count, bins, ignored = plt.hist(s, 14, normed=True) | Random Variable and its Distribution.ipynb | reata/ProbabilityAndStatistics | mit |
5. 均匀分布 Uniform Distribution
若连续型随机变量 X 具有概率密度
$$ f(x) =\left{
\begin{aligned}
& \frac{1}{b-a}, & a < x < b, \
& 0, & 其它 \
\end{aligned}
\right.
$$
则称 X 在区间(a, b)上服从均匀分布，记为$X \sim U(a, b)$
numpy.random.uniform函数可以根据均匀分布进行抽样： | # 取a = -1, b = 0, 样本数10000
a, b = -1, 0
s = np.random.uniform(a, b, 10000)
# 所有样本的值均大于a
np.all(s >= a)
# 所有样本的值均小于b
np.all(s < b)
# 绘制样本直方图及密度函数
count, bins, ignored = plt.hist(s, 15, normed=True)
plt.plot(bins, np.ones_like(bins) / (b - a), linewidth=2, color='r')
plt.show() | Random Variable and its Distribution.ipynb | reata/ProbabilityAndStatistics | mit |
6. 指数分布 Exponential Distribution
若连续型随机变量 X 具有概率密度
$$ f(x) =\left{
\begin{aligned}
& \frac{1}{\theta}e^{-\frac{x}{\theta}}, & x > 0, \
& 0, & 其它 \
\end{aligned}
\right.
$$
其中$\theta > 0$为常数，则称 X 服从参数为$\theta$的指数分布。
numpy.random.exponential函数可以根据均匀分布进行抽样： | # 取theta = 1，绘制样本直方图及密度函数
theta = 1
f = lambda x: math.e ** (-x / theta) / theta
s = np.random.exponential(theta, 10000)
count, bins, ignored = plt.hist(s, 100, normed=True)
plt.plot(bins, f(bins), linewidth=2, color='r')
plt.show() | Random Variable and its Distribution.ipynb | reata/ProbabilityAndStatistics | mit |
7. 正态分布 Normal Distribution
若连续型随机变量 X 的概率密度为
$$ f(x) = \frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-\mu)^2}{2\sigma^2}}, -\infty < x < \infty $$
其中$\mu, \sigma(\sigma > 0)$为常数，则称 X 服从参数为$\mu, \sigma$的正态分布或高斯（Gauss）分布，记为$X \sim N(\mu, \sigma^2)$。
f(x)的图形具有以下性质：
曲线关于$x = \mu$对称。这表明对于任意$h > 0$有
$$ P{\mu - h < X \leq \mu } =... | # 取均值0，标准差0.1
mu, sigma = 0, 0.1
s = np.random.normal(mu, sigma, 1000)
# 验证均值
abs(mu - np.mean(s)) < 0.01
# 验证标准差
abs(sigma - np.std(s, ddof=1)) < 0.01
# 绘制样本直方图及密度函数
count, bins, ignored = plt.hist(s, 30, normed=True)
plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (bins - mu)**2 / (2 * sigma**2) ), linew... | Random Variable and its Distribution.ipynb | reata/ProbabilityAndStatistics | mit |
<img align=left src="files/images/pyspark-page2.svg" width=500 height=500 /> | # print Spark version
print("pyspark version:" + str(sc.version)) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.map">
<img align=left src="files/images/pyspark-page3.svg" width=500 height=500 />
</a> | # map
x = sc.parallelize([1,2,3]) # sc = spark context, parallelize creates an RDD from the passed object
y = x.map(lambda x: (x,x**2))
print(x.collect()) # collect copies RDD elements to a list on the driver
print(y.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.flatMap">
<img align=left src="files/images/pyspark-page4.svg" width=500 height=500 />
</a> | # flatMap
x = sc.parallelize([1,2,3])
y = x.flatMap(lambda x: (x, 100*x, x**2))
print(x.collect())
print(y.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.mapPartitions">
<img align=left src="files/images/pyspark-page5.svg" width=500 height=500 />
</a> | # mapPartitions
x = sc.parallelize([1,2,3], 2)
def f(iterator): yield sum(iterator)
y = x.mapPartitions(f)
print(x.glom().collect()) # glom() flattens elements on the same partition
print(y.glom().collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.mapPartitionsWithIndex">
<img align=left src="files/images/pyspark-page6.svg" width=500 height=500 />
</a> | # mapPartitionsWithIndex
x = sc.parallelize([1,2,3], 2)
def f(partitionIndex, iterator): yield (partitionIndex,sum(iterator))
y = x.mapPartitionsWithIndex(f)
print(x.glom().collect()) # glom() flattens elements on the same partition
print(y.glom().collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.getNumPartitions">
<img align=left src="files/images/pyspark-page7.svg" width=500 height=500 />
</a> | # getNumPartitions
x = sc.parallelize([1,2,3], 2)
y = x.getNumPartitions()
print(x.glom().collect())
print(y) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.filter">
<img align=left src="files/images/pyspark-page8.svg" width=500 height=500 />
</a> | # filter
x = sc.parallelize([1,2,3])
y = x.filter(lambda x: x%2 == 1) # filters out even elements
print(x.collect())
print(y.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.distinct">
<img align=left src="files/images/pyspark-page9.svg" width=500 height=500 />
</a> | # distinct
x = sc.parallelize(['A','A','B'])
y = x.distinct()
print(x.collect())
print(y.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.sample">
<img align=left src="files/images/pyspark-page10.svg" width=500 height=500 />
</a> | # sample
x = sc.parallelize(range(7))
ylist = [x.sample(withReplacement=False, fraction=0.5) for i in range(5)] # call 'sample' 5 times
print('x = ' + str(x.collect()))
for cnt,y in zip(range(len(ylist)), ylist):
print('sample:' + str(cnt) + ' y = ' + str(y.collect())) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.takeSample">
<img align=left src="files/images/pyspark-page11.svg" width=500 height=500 />
</a> | # takeSample
x = sc.parallelize(range(7))
ylist = [x.takeSample(withReplacement=False, num=3) for i in range(5)] # call 'sample' 5 times
print('x = ' + str(x.collect()))
for cnt,y in zip(range(len(ylist)), ylist):
print('sample:' + str(cnt) + ' y = ' + str(y)) # no collect on y | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.union">
<img align=left src="files/images/pyspark-page12.svg" width=500 height=500 />
</a> | # union
x = sc.parallelize(['A','A','B'])
y = sc.parallelize(['D','C','A'])
z = x.union(y)
print(x.collect())
print(y.collect())
print(z.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.intersection">
<img align=left src="files/images/pyspark-page13.svg" width=500 height=500 />
</a> | # intersection
x = sc.parallelize(['A','A','B'])
y = sc.parallelize(['A','C','D'])
z = x.intersection(y)
print(x.collect())
print(y.collect())
print(z.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.sortByKey">
<img align=left src="files/images/pyspark-page14.svg" width=500 height=500 />
</a> | # sortByKey
x = sc.parallelize([('B',1),('A',2),('C',3)])
y = x.sortByKey()
print(x.collect())
print(y.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.sortBy">
<img align=left src="files/images/pyspark-page15.svg" width=500 height=500 />
</a> | # sortBy
x = sc.parallelize(['Cat','Apple','Bat'])
def keyGen(val): return val[0]
y = x.sortBy(keyGen)
print(y.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.glom">
<img align=left src="files/images/pyspark-page16.svg" width=500 height=500 />
</a> | # glom
x = sc.parallelize(['C','B','A'], 2)
y = x.glom()
print(x.collect())
print(y.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.cartesian">
<img align=left src="files/images/pyspark-page17.svg" width=500 height=500 />
</a> | # cartesian
x = sc.parallelize(['A','B'])
y = sc.parallelize(['C','D'])
z = x.cartesian(y)
print(x.collect())
print(y.collect())
print(z.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.groupBy">
<img align=left src="files/images/pyspark-page18.svg" width=500 height=500 />
< | # groupBy
x = sc.parallelize([1,2,3])
y = x.groupBy(lambda x: 'A' if (x%2 == 1) else 'B' )
print(x.collect())
print([(j[0],[i for i in j[1]]) for j in y.collect()]) # y is nested, this iterates through it | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.pipe">
<img align=left src="files/images/pyspark-page19.svg" width=500 height=500 />
</a> | # pipe
x = sc.parallelize(['A', 'Ba', 'C', 'AD'])
y = x.pipe('grep -i "A"') # calls out to grep, may fail under Windows
print(x.collect())
print(y.collect()) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.foreach">
<img align=left src="files/images/pyspark-page20.svg" width=500 height=500 />
</a> | # foreach
from __future__ import print_function
x = sc.parallelize([1,2,3])
def f(el):
'''side effect: append the current RDD elements to a file'''
f1=open("./foreachExample.txt", 'a+')
print(el,file=f1)
open('./foreachExample.txt', 'w').close() # first clear the file contents
y = x.foreach(f) # writes ... | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.foreachPartition">
<img align=left src="files/images/pyspark-page21.svg" width=500 height=500 />
</a> | # foreachPartition
from __future__ import print_function
x = sc.parallelize([1,2,3],5)
def f(parition):
'''side effect: append the current RDD partition contents to a file'''
f1=open("./foreachPartitionExample.txt", 'a+')
print([el for el in parition],file=f1)
open('./foreachPartitionExample.txt', 'w').cl... | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.collect">
<img align=left src="files/images/pyspark-page22.svg" width=500 height=500 />
</a> | # collect
x = sc.parallelize([1,2,3])
y = x.collect()
print(x) # distributed
print(y) # not distributed | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.reduce">
<img align=left src="files/images/pyspark-page23.svg" width=500 height=500 />
</a> | # reduce
x = sc.parallelize([1,2,3])
y = x.reduce(lambda obj, accumulated: obj + accumulated) # computes a cumulative sum
print(x.collect())
print(y) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.fold">
<img align=left src="files/images/pyspark-page24.svg" width=500 height=500 />
</a> | # fold
x = sc.parallelize([1,2,3])
neutral_zero_value = 0 # 0 for sum, 1 for multiplication
y = x.fold(neutral_zero_value,lambda obj, accumulated: accumulated + obj) # computes cumulative sum
print(x.collect())
print(y) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.aggregate">
<img align=left src="files/images/pyspark-page25.svg" width=500 height=500 />
</a> | # aggregate
x = sc.parallelize([2,3,4])
neutral_zero_value = (0,1) # sum: x+0 = x, product: 1*x = x
seqOp = (lambda aggregated, el: (aggregated[0] + el, aggregated[1] * el))
combOp = (lambda aggregated, el: (aggregated[0] + el[0], aggregated[1] * el[1]))
y = x.aggregate(neutral_zero_value,seqOp,combOp) # computes (cu... | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.max">
<img align=left src="files/images/pyspark-page26.svg" width=500 height=500 />
</a> | # max
x = sc.parallelize([1,3,2])
y = x.max()
print(x.collect())
print(y) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.min">
<img align=left src="files/images/pyspark-page27.svg" width=500 height=500 />
</a> | # min
x = sc.parallelize([1,3,2])
y = x.min()
print(x.collect())
print(y) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.sum">
<img align=left src="files/images/pyspark-page28.svg" width=500 height=500 />
</a> | # sum
x = sc.parallelize([1,3,2])
y = x.sum()
print(x.collect())
print(y) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.count">
<img align=left src="files/images/pyspark-page29.svg" width=500 height=500 />
</a> | # count
x = sc.parallelize([1,3,2])
y = x.count()
print(x.collect())
print(y) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.histogram">
<img align=left src="files/images/pyspark-page30.svg" width=500 height=500 />
</a> | # histogram (example #1)
x = sc.parallelize([1,3,1,2,3])
y = x.histogram(buckets = 2)
print(x.collect())
print(y)
# histogram (example #2)
x = sc.parallelize([1,3,1,2,3])
y = x.histogram([0,0.5,1,1.5,2,2.5,3,3.5])
print(x.collect())
print(y) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
<a href="http://spark.apache.org/docs/1.2.0/api/python/pyspark.html#pyspark.RDD.mean">
<img align=left src="files/images/pyspark-page31.svg" width=500 height=500 />
</a> | # mean
x = sc.parallelize([1,3,2])
y = x.mean()
print(x.collect())
print(y) | notes/2-pyspark-rdd-examples.ipynb | dsiufl/2015-Fall-Hadoop | mit |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.