Ejemplos de convert_msa en Python

Ejemplo n.º 1

Archivo: main_structure.py Proyecto: rfarouni/Other_Code

import phase_2_model

import pickle

if __name__ == "__main__":

    #Get data

    #Load the pickled weights
    #Random for now
    experts_weights = np.zeros((L, L, n_aa, n_aa))

    #RASH
    L = 166
    msa_file = home + '/Documents/Protein_data/RASH/RASH_HUMAN2_833a6535-26d0-4c47-8463-7970dae27a32_evfold_result/alignment/RASH_HUMAN2_RASH_HUMAN2_jackhmmer_e-10_m30_complete_run.fa'
    msa, n_aa = protein_model_tools.convert_msa(L, msa_file)
    print len(msa), len(msa[0])

    #Learn hyperparameters - Grid search
    nu_list = [.1]
    rho_list = [.00001]
    sigma_adj_list = [.01]
    sigma_repel_list = [.1]

    best_error = -1
    best_hypers = []
    best_coords = []

    for iter1 in range(1):
        print 'Iteration ' + str(iter1)

Ejemplo n.º 2

rao = 0
mac = 1


if rao == 1:
	msa_file = home + '/protein_data/RASH_HUMAN2_RASH_HUMAN2_jackhmmer_e-10_m30_complete_run.fa'
if mac == 1:
	msa_file = home + '/Documents/Protein_data/RASH/RASH_HUMAN2_833a6535-26d0-4c47-8463-7970dae27a32_evfold_result/alignment/RASH_HUMAN2_RASH_HUMAN2_jackhmmer_e-10_m30_complete_run.fa'


#RASH
L = 166
msa, n_aa = tools.convert_msa(L, msa_file)
print len(msa), len(msa[0]), n_aa


#Convert to matrix
msa_vectors = []
for samp in range(2000):
	msa_vectors.append(np.ndarray.flatten(tools.convert_samp_to_one_hot(msa[samp], n_aa)))
msa_vectors = np.array(msa_vectors)
print msa_vectors.shape

#PCA
pca = PCA(n_components=20)
pca.fit(msa_vectors[1000:])
a_samps_pca = pca.transform(msa_vectors[1000:])
b_samps_pca = pca.transform(msa_vectors[:1000])

Ejemplo n.º 3

Archivo: main_experts.py Proyecto: chriscremer/Other_Code

if __name__ == "__main__":


	#Get data

	#MAKE MY OWN DATA
	# L=166
	# n_aa=22
	# msa = protein_model_tools.make_data(n_samps=20000, L=166, n_aa=22)

	#RASH
	L = 166
	msa_file = home + '/Documents/Protein_data/RASH/RASH_HUMAN2_833a6535-26d0-4c47-8463-7970dae27a32_evfold_result/alignment/RASH_HUMAN2_RASH_HUMAN2_jackhmmer_e-10_m30_complete_run.fa'
	msa, n_aa = protein_model_tools.convert_msa(L, msa_file)
	print len(msa), len(msa[0])


	#Learn the experts
	experts_weights = np.zeros((L,L,n_aa,n_aa))
	# experts_biases = np.zeros((L,L,n_aa))

	total = L*L

	for i in range(L):
		for j in range(L):

			if i == j:
				continue