mzdravkov’s gists

mzdravkov / biostatistics_project.r

Last active April 18, 2023 09:48

biostatistics_project.r

	Dataset <- read.csv("Dataset.csv", stringsAsFactors=TRUE, header=TRUE)

	Dataset <- Dataset[Dataset$Assignment != "",]

	View(Dataset)

	drugs <- data.frame(
	Name = c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "AA", "AB", "AC", "AD"),
	Color = c("Red", "Red", "Green", "Yellow", "Red", "Yellow", "Red", "Red", "Red", "Red", "Green", "Red", "Red", "Red", "Green", "Red", "Red", "Green", "Yellow", "Red", "Red", "Yellow", "Yellow", "Red", "Yellow", "Red", "Yellow", "Red", "Red", "Green")
	)

mzdravkov / Dataset.csv

Created April 17, 2023 21:42

Dataset.csv

We can make this file beautiful and searchable if this error is corrected: It looks like row 8 should actually have 43 columns, instead of 6 in line 7.

	ID,GENDER,AGE,RACE/ETHNICITY,Diagnosis,MD,Assignment,EMR,LOS,RAR,A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q,R,S,T,U,V,W,X,Y,Z,AA,AB,AC,AD,# Psychotropic Medications,# Administrations,Therapeutic Guidances
	1,F,49,W,"MDD, Recurrent, Unspecified",L,G,C,70,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,6,EG
	2,F,21,W,"MDD, Recurrent, Unspecified",A,G,C,309,0,0,0,0,1,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,3,27,CT
	3,M,28,L,"MDD, Single Episode, Severe With Psychotic Features",I,G,C,376,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,5,0,26,0,0,0,8,0,0,0,15,0,4,0,0,6,64,CT
	4,F,63,L,Depressive Disorder NOS,L,G,C,115,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,5,0,0,0,0,3,14,CT
	5,M,34,L,"MDD, Single Episode, Severe With Psychotic Features",G,S,C,120,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,3,11,EG
	6,F,24,L,"MDD, Single Episode,Severe Without Psychotic Features",T,S,C,120,0,0,5,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,4,13,EG
	7,M,42,W,"MDD, Single Episode, Unspecified",

mzdravkov / mobilenet_model.py

Created November 15, 2023 18:27

	from keras.constraints import max_norm

	def get_res_blocks(definitions, input, l2_factor=0.0001, kernel_constraint_norm=2.0):
	hiddenx = tf.keras.layers.Dense(definitions[0],
	activation='relu',
	kernel_regularizer=tf.keras.regularizers.l2(l2_factor),
	kernel_constraint=max_norm(kernel_constraint_norm),
	bias_initializer=he_init)(input)
	hiddeny = tf.keras.layers.Dense(definitions[0],
	# activation='relu',

mzdravkov / build_sequences.py

Created December 10, 2023 22:23

Build time series sequences

	def build_sequences(time_series, valid_periods, categories, train_size, test_size):
	"""
	Creates all possible test sequences with size <test_size> which have
	a training sequence of <train_size> in front.
	"""
	X = []
	y = []
	final_categories = []
	for ts, range, category in zip(time_series, valid_periods, categories):
	valid_ts = cut_valid(ts, range)

mzdravkov / fourier_time_series_forecasting.py

Created December 16, 2023 20:23

Time series forecasting with fourier transformations

	def build_sequences(time_series, valid_periods, categories, train_size, test_size):
	"""
	Creates all possible test sequences with size <test_size> which have
	a training sequence of <train_size> in front.
	"""
	X = []
	y = []
	final_categories = []
	for ts, range, category in zip(time_series, valid_periods, categories):
	valid_ts = cut_valid(ts, range)

mzdravkov / build_sequences2.py

Created December 22, 2023 11:42

build_sequences_no_interpolation

	def build_sequences(time_series, valid_periods, categories, train_size, test_size):
	"""
	Creates all possible test sequences with size <test_size> which have
	a training sequence of <train_size> in front.
	"""
	X = []
	y = []
	final_categories = []
	for ts, range, category in zip(time_series, valid_periods, categories):
	valid_ts = cut_valid(ts, range)

Mihail Zdravkov mzdravkov