Federico Vaggi FedericoV
FedericoV
/ Lasso vs subgradient
Created
August 24, 2020 06:04
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| from sklearn.datasets import make_regression | |
| from sklearn.linear_model import Lasso | |
| import numpy as np | |
| from scipy.optimize import approx_fprime, check_grad, minimize | |
| X, y, w_true = make_regression(n_samples=200, n_features=500, random_state=0, coef=True) | |
| w_init = np.ones_like(w_true) | |
| lam = 10 | |
| def l(w, X, y, lam=10): |
FedericoV
/ gist:bef857ccbe3ee07e99767968bdd40b61
Created
May 14, 2020 16:01
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| def _lambdifygenerated(_Dummy_169, _Dummy_168): | |
| [[beta_0_0, beta_0_1, beta_0_2, beta_0_3, beta_0_4, beta_0_5, beta_0_6, beta_0_7, beta_0_8, beta_0_9, beta_0_10, beta_0_11, beta_0_12, beta_0_13, beta_0_14, beta_0_15, beta_0_16, beta_0_17, beta_0_18, beta_0_19, beta_0_20], [beta_1_0, beta_1_1, beta_1_2, beta_1_3, beta_1_4, beta_1_5, beta_1_6, beta_1_7, beta_1_8, beta_1_9, beta_1_10, beta_1_11, beta_1_12, beta_1_13, beta_1_14, beta_1_15, beta_1_16, beta_1_17, beta_1_18, beta_1_19, beta_1_20], [beta_2_0, beta_2_1, beta_2_2, beta_2_3, beta_2_4, beta_2_5, beta_2_6, beta_2_7, beta_2_8, beta_2_9, beta_2_10, beta_2_11, beta_2_12, beta_2_13, beta_2_14, beta_2_15, beta_2_16, beta_2_17, beta_2_18, beta_2_19, beta_2_20], [beta_3_0, beta_3_1, beta_3_2, beta_3_3, beta_3_4, beta_3_5, beta_3_6, beta_3_7, beta_3_8, beta_3_9, beta_3_10, beta_3_11, beta_3_12, beta_3_13, beta_3_14, beta_3_15, beta_3_16, beta_3_17, beta_3_18, beta_3_19, beta_3_20], [beta_4_0, beta_4_1, beta_4_2, beta_4_3, beta_4_4, beta_4_5, beta_4_6, beta_4_7 |
FedericoV
/ gist:b1a1901a3fba5484edb51bcbe797cfb4
Created
February 13, 2020 19:42
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| feature_hierarchies: Optional[Union[hierarchy_type], List[hierarchy_type]] | |
| if feature_hierarchies: | |
| if not isinstance(feature_hierarchies, list): | |
| graph_hierarchies = [graph_from_hierarchy(feature_hierarchies)] | |
| else: | |
| graph_hierarchies = [graph_from_hierarchy(h) for h in feature_hierarchies] |
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| 186590d34d15:FBA_Analytics_Utils vaggi$ pyre --debug check | |
| 2018-05-14 15:13:57,354 DEBUG No configuration found at `/Users/vaggi/Code_Libraries/FBA_Analytics_Utils/.pyre_configuration.local`. | |
| 2018-05-14 15:13:57,354 DEBUG No configuration found at `.pyre_configuration.local`. | |
| 2018-05-14 15:13:57,354 DEBUG Reading configuration `.pyre_configuration`... | |
| 2018-05-14 15:13:57,355 DEBUG Found source directories `.` | |
| 2018-05-14 15:13:57,356 DEBUG Running `/Users/vaggi/anaconda3/bin/pyre.bin check -debug -sequential -project-root /Users/vaggi/Code_Libraries/FBA_Analytics_Utils -workers 1 -search-path /Users/vaggi/anaconda3/lib/pyre_check/typeshed/stdlib/ .` | |
| 2018-05-14 15:13:57,728 ERROR Client exited with error code -5: | |
FedericoV
/ gist:30c1b2f20764bc6092c70a5759c1c33c
Created
October 22, 2016 21:17
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| vol*(0.00577622650467*Ydj1*kb - (Ydj1*kb + kd*vol)*(kd + kr + 0.00577622650467))*(Ydj1*kb + 0.069314718056*vol)**2*(Ydj1*kb + kd*vol)*((YC*kr + 0.069314718056*YC + YP*kd + YP*kr - 0.00577622650467*Ydj1 + 2*Ys*vol)/vol - (-Ydj1*cln3*kb - Ydj1*kb*prot + vol*(YC*kr + 0.069314718056*YC + YP*kd + YP*kr - 0.00577622650467*Ydj1 + Ys*vol))/vol**2)*(Ydj1*cln3*kb**2/(vol**2*(-Ydj1*kb/vol - 0.069314718056)) + cln3*kb/vol)/(Ydj1*cln3*kb**2*(0.00577622650467*Ydj1*kb - (kr + 0.07509094456067)*(Ydj1*kb + 0.069314718056*vol))*(0.00577622650467*Ydj1*kb - (Ydj1*kb + kd*vol)*(kd + kr + 0.00577622650467))*(Ydj1*kb + kd*vol) + Ydj1*kb**2*prot*(0.00577622650467*Ydj1*kb - (kr + 0.07509094456067)*(Ydj1*kb + 0.069314718056*vol))*(0.00577622650467*Ydj1*kb - (Ydj1*kb + kd*vol)*(kd + kr + 0.00577622650467))*(Ydj1*kb + 0.069314718056*vol) - 0.069314718056*cln3*kb*vol*(-0.00577622650467*Ydj1*kb + (kr + 0.069314718056)*(Ydj1*kb + 0.069314718056*vol))*(0.00577622650467*Ydj1*kb - (Ydj1*kb + kd*vol)*(kd + kr + 0.00577622650467))*(Ydj1*kb + kd |
FedericoV
/ Grid_Search Functionality?
Created
June 17, 2016 11:13
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| for train_idx, test_idx in cv: | |
| X_train, y_train = X[train_idx], y[train_idx] | |
| X_test, y_test = X[test_idx], y[test_idx] | |
| pip.fit(X_train, y_train) | |
| y_pred = pip.predict(X_test) | |
| print (roc_auc_score(y_test, y_pred)) | |
| 0.70326179109 |
FedericoV
/ pg-pong.py
Created
June 3, 2016 13:26
— forked from karpathy/pg-pong.py
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
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| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
FedericoV
/ gist:05ad9af84a0ff192187c
Created
March 25, 2016 20:17
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| conv_model = Sequential() | |
| conv_model.add(Embedding(max_features, embedding_size, input_length=maxlen)) | |
| conv_model.add(Dropout(0.25)) | |
| conv_model.add(Convolution1D(nb_filter=nb_filter, | |
| filter_length=filter_length, | |
| border_mode='valid', | |
| activation='relu', | |
| subsample_length=1)) | |
| conv_model.add(MaxPooling1D(pool_length=pool_length)) | |
| conv_model.add(LSTM(lstm_output_size)) |
FedericoV
/ gist:6533c311bf4423cd7f38
Created
March 24, 2016 13:36
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| 2016-03-24T14:28:11.662+0100 connected to: localhost | |
| 2016-03-24T14:28:14.661+0100 yasp_dump.December 290.5 MB | |
| 2016-03-24T14:28:17.661+0100 yasp_dump.December 581.2 MB | |
| 2016-03-24T14:28:20.661+0100 yasp_dump.December 882.1 MB | |
| 2016-03-24T14:28:23.661+0100 yasp_dump.December 1.1 GB | |
| 2016-03-24T14:28:26.661+0100 yasp_dump.December 1.3 GB | |
| 2016-03-24T14:28:29.661+0100 yasp_dump.December 1.6 GB | |
| 2016-03-24T14:28:32.661+0100 yasp_dump.December 1.9 GB | |
| 2016-03-24T14:28:35.661+0100 yasp_dump.December 2.1 GB | |
| 2016-03-24T14:28:38.661+0100 yasp_dump.December 2.3 GB |
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| { | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Julia and its Ecosystem\n", | |
| "-------------------------------------\n", |
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