repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/load.py | src/rwkvstic/load.py |
from rwkvstic.agnostic.agnosticRwkv import AgnosticRWKV
from rwkvstic.agnostic.rnn import RnnRWKV
from rwkvstic.helpers.loadWeights import loadWeights
from rwkvstic.agnostic.backends import Backends
from rwkvstic.interOpLoaders import tflite, torchscript, prequantized, preJax, rwkvRs, onnx, chatRWKV
from rwkvstic.rwk... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/preQuantize.py | src/rwkvstic/preQuantize.py |
from rwkvstic.helpers.loadWeights import loadWeights
from rwkvstic.rwkvMaster import RWKVMaster
import torch
import gc
import inquirer
import os
# set torch threads to 8
torch.set_num_threads(8)
def preQuantized(path=None, chunksize=32, useLogFix=True) -> RWKVMaster:
if (path == None):
files = os.list... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/preJax.py | src/rwkvstic/preJax.py |
from rwkvstic.helpers.loadWeights import loadWeights
from rwkvstic.rwkvMaster import RWKVMaster
import torch
import gc
import inquirer
import os
# set torch threads to 8
torch.set_num_threads(8)
def preJax(path=None) -> RWKVMaster:
if (path == None):
files = os.listdir()
# filter by ending in ... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/__main__.py | src/rwkvstic/__main__.py | from sys import argv
if __name__ == '__main__':
import rwkvstic.preQuantize as pq
import rwkvstic.preJax as pj
args = {
"chunksize": 32 if "--cs" not in argv else int(argv[-1].split("=")[1]),
"useLogFix": "--nologfix" not in argv,
}
if ("--server" in argv):
import rwkvstic.s... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/rwkvMaster.py | src/rwkvstic/rwkvMaster.py |
import tqdm
import rwkvstic.tokenizer as tokenizer
from typing import List
# this is for like, being useful
import time
def clone (x):
return x.clone() if hasattr(x, "clone") else x
class RWKVMaster():
def __init__(self, model, emptyState, initTensor=lambda x: x, intTensor=lambda x: x, sampler=None, tokP... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/bench.py | src/rwkvstic/bench.py | import time
import torch
from rwkvstic.load import RWKV
def bechmark():
# choose a file to load with the file picker dialog
import tkinter as tk
from tkinter import filedialog
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename(
initialdir = "./",
title = "Sele... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/__init__.py | src/rwkvstic/__init__.py | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false | |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/server.py | src/rwkvstic/server.py | # command = python3 -m rwkvstic --server **args
import torch
from sys import argv
def fixDtype(x): return torch.float32 if x == "float32" else torch.float64 if x == "float64" else torch.bfloat16 if x == "bfloat16" else torch.float16 if x == "float16" else x
def fixNumbers(x): return int(x) if type(x) == str and x.is... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/tools/getBestSetup.py | src/rwkvstic/tools/getBestSetup.py | import torch
mat = torch.randn(1000, 1000).float().cuda()
vec = torch.randn(1000).float().cuda()
matbfloat = mat.bfloat16().cuda()
vecbfloat = vec.bfloat16().cuda()
mathalf = mat.half().cuda()
vechalf = vec.half().cuda()
rounds = 1000
# warmup
for i in range(1000):
x = torch.mv(mat, vec)
# time the bfloat16 ma... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/tools/getBestSetupCpu.py | src/rwkvstic/tools/getBestSetupCpu.py | import time
import torch
mat = torch.randn(1000, 1000).float()
vec = torch.randn(1000).float()
matbfloat = mat.bfloat16()
vecbfloat = vec.bfloat16()
matdouble = mat.double()
vecdouble = vec.double()
rounds = 1000
# warmup
for i in range(1000):
x = torch.mv(mat, vec)
# time the bfloat16 matmul
start = time.t... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/rnn.py | src/rwkvstic/agnostic/rnn.py | from rwkvstic.agnostic.backends.base import module
from typing import Dict
def RnnRWKV(ops: module, *args):
class myRWKV(ops.module):
@ ops.initfunc
def __init__(self, w: Dict[str, ops.TensorType]):
super(myRWKV, self).__init__()
print("Legacy RWKV")
self.ops ... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/agnosticRwkv.py | src/rwkvstic/agnostic/agnosticRwkv.py | from rwkvstic.agnostic.backends.base import module
from typing import Dict, List
import torch
# allow tensor core ops
torch.backends.cudnn.benchmark = True
# allow fp16 ops
torch.backends.cudnn.allow_tf32 = True
# allow use of cuDNN
torch.backends.cudnn.enabled = True
def AgnosticRWKV(ops: module, *args):
class m... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/__init__.py | src/rwkvstic/agnostic/__init__.py | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false | |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/samplers/typical.py | src/rwkvstic/agnostic/samplers/typical.py | def typical(logits, temp=1.0, tau=0.95, **kwargs):
import torch
# do it in pytorch
import numpy as np
probs = torch.nn.functional.softmax(logits.float(), dim=-1)
logits = -torch.log(probs)
ent = torch.nansum(logits * probs, dim=-1, keepdim=True)
shifted_logits = torch... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/samplers/numpy.py | src/rwkvstic/agnostic/samplers/numpy.py |
def npsample(ozut, temp: float = 1.0, top_p_usual: float = 0.8) -> int:
import numpy as np
from scipy.special import softmax
try:
ozut = ozut.numpy()
except:
try:
ozut = ozut.cpu().numpy()
except:
ozut = np.array(ozut)
# out[self.UNKNOWN_CHAR] = -f... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/samplers/__init__.py | src/rwkvstic/agnostic/samplers/__init__.py | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false | |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/samplers/torch.py | src/rwkvstic/agnostic/samplers/torch.py | def torchsample(ozut, temp=1.0, top_p_usual=0.8) -> int:
import torch
# do it in pytorch
probs = torch.softmax(ozut, dim=-1)
sorted_probs, indices = torch.sort(probs, descending=True)
cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
cutoff = sorted_probs[torch.argmax(
cumulative_pr... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/opt.py | src/rwkvstic/agnostic/backends/opt.py | import torch
from rwkvstic.agnostic.backends.modules.matmul import Linear
from rwkvstic.agnostic.backends.modules.layernorm import LayerNorm
from rwkvstic.agnostic.backends.modules.block import Block
from rwkvstic.agnostic.backends.modules.emb import RwkvEmb, RwkvModule
from tqdm import tqdm
import os
current_path = ... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/jax.py | src/rwkvstic/agnostic/backends/jax.py |
import rwkvstic.agnostic.backends.base as RWKVOp
class RWKVNumpyOps(RWKVOp.module):
def __init__(self, layers, embed, *args, **kwargs):
import numpy as np
super().__init__(layers, embed, *args, **kwargs)
self.initTensor = lambda x: x.float().cpu().numpy()
self.sqrt = np.sqr... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/tensorflow.py | src/rwkvstic/agnostic/backends/tensorflow.py | import inquirer
import os
import rwkvstic.agnostic.backends.base as RWKVOp
class RWKVTFOps(RWKVOp.module):
def __init__(self, layers, embed, *args, useGPU: bool = None, **kwargs):
try:
import tensorflow as tf
except:
inst = inquirer.confirm(
"Tensorflow not ... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/coreml.py | src/rwkvstic/agnostic/backends/coreml.py | from rwkvstic.agnostic.backends.base import module
class RWKVCoreMLOps(module):
def __init__(self, layers, embed, *args, **kwargs):
super().__init__(layers, embed, *args, **kwargs)
from coremltools.converters.mil.mil import Builder as mb
from coremltools.converters.mil.mil import Program,... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/__init__.py | src/rwkvstic/agnostic/backends/__init__.py | from rwkvstic.agnostic.backends.torch import RWKVCudaOps, RWKVPTTSExportOps, RWKVCudaDeepspeedOps, RWKVMpsOps
from rwkvstic.agnostic.backends.jax import RWKVJaxOps, RWKVNumpyOps, RWKVCuPyOps, RWKVCuPyQuantOps
from rwkvstic.agnostic.backends.tensorflow import RWKVTFExport, RWKVTFOps
from rwkvstic.agnostic.backends.base ... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/torch.py | src/rwkvstic/agnostic/backends/torch.py |
from typing import List, Union
import rwkvstic.agnostic.backends.base as RWKVOp
class RWKVPTOps(RWKVOp.module):
def __init__(self, layers, embed, *args, dtype=None, **kwargs):
import torch
import inquirer
super().__init__(layers, embed, dtype=dtype, *args, **kwargs)
q = [inquir... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/base.py | src/rwkvstic/agnostic/backends/base.py | from typing import List, Dict, Union
class module:
def __init__(self, layers, embed, *args, useLogFix=True, **kwargs):
from rwkvstic.agnostic.samplers.numpy import npsample
self.VectorType = List[float]
self.useLogFix = useLogFix
self.MatrixType = List[List[float]]
self.Te... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/onnx.py | src/rwkvstic/agnostic/backends/onnx.py | import numpy as np
import rwkvstic.agnostic.backends.base as RWKVOp
class RWKVOnnxOps(RWKVOp.rnnmodule):
def __init__(self, layers, embed, *args, dtype=None, **kwargs):
import onnx
super().__init__(layers, embed, *args, **kwargs)
print("embed ", embed)
import torch
if dt... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/cuda/load.py | src/rwkvstic/agnostic/backends/cuda/load.py | def loadCustomCudaModule():
from torch.utils.cpp_extension import load
import os
current_path = os.path.dirname(os.path.abspath(__file__))
load(
name=f"wkv_cuda",
sources=[f"{current_path}/wrapper.cpp",
f"{current_path}/operators.cu",
f"{current_... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/modules/block.py | src/rwkvstic/agnostic/backends/modules/block.py | from rwkvstic.agnostic.backends.modules.base import RwkvModule
from rwkvstic.agnostic.backends.modules.layernorm import LayerNorm
from rwkvstic.agnostic.backends.modules.matmul import Linear, Linear3
from rwkvstic.agnostic.backends.modules.wkv import WKV
import torch
class Block(RwkvModule):
def __init__(se... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/modules/layernorm.py | src/rwkvstic/agnostic/backends/modules/layernorm.py |
from rwkvstic.agnostic.backends.modules.base import RwkvModule
import torch
class LayerNorm(RwkvModule):
def __init__(self, weight, bias):
super(LayerNorm, self).__init__()
self.weight = weight.clone().to(torch.float64)
self.bias = bias.clone().to(torch.float64)
self.device = torch.... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/modules/emb.py | src/rwkvstic/agnostic/backends/modules/emb.py |
from rwkvstic.agnostic.backends.modules.base import RwkvModule
import torch
class RwkvEmb (RwkvModule):
def __init__(self,w):
super().__init__()
self.w = w.clone().cpu()
def forward(self,x):
return self.w[x.cpu()].to(torch.float64)
| python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/modules/base.py | src/rwkvstic/agnostic/backends/modules/base.py | import torch
class RwkvModule(torch.nn.Module):
def __init__(self):
super(RwkvModule, self).__init__()
self.submodules = []
self.subattributes = []
def add_submodule(self, submodule):
self.submodules.append(submodule)
def config(self, config):
pass
| python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/modules/matmul.py | src/rwkvstic/agnostic/backends/modules/matmul.py |
from rwkvstic.agnostic.backends.modules.base import RwkvModule
import torch
from typing import List
class MM8(RwkvModule):
def __init__(self, weight, device, stream = False):
super(MM8, self).__init__()
self.device = device
self.stream = stream
... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/agnostic/backends/modules/wkv.py | src/rwkvstic/agnostic/backends/modules/wkv.py |
from rwkvstic.agnostic.backends.modules.base import RwkvModule
import torch
class CudaWKV(RwkvModule):
def __init__(self):
super(CudaWKV, self).__init__()
self.y = torch.empty((1, 1), device="cpu", memory_format=torch.contiguous_format, dtype=torch.float64)
def forward(self, T: int, C: int, w, ... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/interOpLoaders/torchscript.py | src/rwkvstic/interOpLoaders/torchscript.py | from rwkvstic.rwkvMaster import RWKVMaster
from rwkvstic.agnostic.samplers.numpy import npsample
def initTorchScriptFile(path, tokenizer=None):
import torch
embed = path.split("-")[2].split(".")[0]
layers = path.split("-")[1]
mymodel = torch.jit.load(path)
device = torch.device("cuda" if "gpu" in ... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/interOpLoaders/preJax.py | src/rwkvstic/interOpLoaders/preJax.py |
from rwkvstic.rwkvMaster import RWKVMaster
from rwkvstic.agnostic.agnosticRwkv import AgnosticRWKV
from rwkvstic.agnostic.backends.jax import RWKVJaxOps
def loadPreJax(path, tokenizer=None):
import jax
weights = jax.numpy.load(path, allow_pickle=True)
# filter out the keys that are not .block
weight... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/interOpLoaders/chatRWKV.py | src/rwkvstic/interOpLoaders/chatRWKV.py | from rwkvstic.rwkvMaster import RWKVMaster
from rwkvstic.agnostic.samplers.numpy import npsample
def initRWKVOriginal(path, strategy, tokenizer=None):
from rwkv.model import RWKV
modell = RWKV(path, strategy)
class InterOp():
RnnOnly = False
def forward(self, x, y):
return m... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/interOpLoaders/__init__.py | src/rwkvstic/interOpLoaders/__init__.py | # these are for testing exported models
| python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/interOpLoaders/prequantized.py | src/rwkvstic/interOpLoaders/prequantized.py |
from rwkvstic.rwkvMaster import RWKVMaster
from rwkvstic.agnostic.agnosticRwkv import AgnosticRWKV
def loadPreQuantized(path, tokenizer=None):
raise "Please use version < 2.0 for .pqth files, >2.0 uses .rwkv files, and is up to 8x faster"
| python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/interOpLoaders/tflite.py | src/rwkvstic/interOpLoaders/tflite.py | from rwkvstic.rwkvMaster import RWKVMaster
from rwkvstic.agnostic.samplers.numpy import npsample
def initTFLiteFile(path, tokenizer=None):
import tensorflow.lite as tflite
import tensorflow as tf
interpreter = tflite.Interpreter(
model_path=path)
interpreter.allocate_tensors()
input_det... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/interOpLoaders/rwkvRs.py | src/rwkvstic/interOpLoaders/rwkvRs.py | from rwkvstic.rwkvMaster import RWKVMaster
from rwkvstic.agnostic.samplers.numpy import npsample
def initRwkvRsFile(model_path, tokenizer=None):
import rwkv_rs
import huggingface_hub
import os
if not os.path.exists(model_path):
model_path = huggingface_hub.hf_hub_download(
repo_id=... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/interOpLoaders/onnx.py | src/rwkvstic/interOpLoaders/onnx.py |
from rwkvstic.agnostic.samplers.numpy import npsample
from rwkvstic.rwkvMaster import RWKVMaster
def initONNXFile(path, tokenizer=None, useAllAvailableProviders=True):
import onnxruntime as rt
# session execution provider options
sess_options = rt.SessionOptions()
print(rt.get_available_providers()... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/helpers/loadWeights.py | src/rwkvstic/helpers/loadWeights.py |
from rwkvstic.agnostic.backends import Backends
from rwkvstic.agnostic.backends.base import module
from typing import Dict
from tqdm import tqdm
import inquirer
def loadWeights(mode, path, *args, processEmb=True, **kwargs):
import torch
n_layer = 0
w: Dict[str, torch.Tensor] = torch.load(
path... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/tokenizer/__init__.py | src/rwkvstic/tokenizer/__init__.py | from transformers import PreTrainedTokenizerFast
import os
path = "20B_tokenizer.json"
path = os.path.join(os.path.dirname(__file__), path)
def tokenizer(x=None): return PreTrainedTokenizerFast(
tokenizer_file=x if x is not None else path)
| python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/training/model.py | src/rwkvstic/training/model.py | import torch
from rwkvstic.training.modules.block import Block
class RWKV(torch.nn.Module):
def __init__(self,dims,layers, head, T):
super(RWKV, self).__init__()
print("Training RWKV")
# head = 50277
self.emb = torch.nn.Embedding... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/training/modules/block.py | src/rwkvstic/training/modules/block.py |
from rwkvstic.training.modules.wkv import wkv_power
import torch
class Block(torch.nn.Module):
def __init__(self, dims,T):
super(Block, self).__init__()
self.ln1 = torch.nn.LayerNorm((dims,))
self.ln2 = torch.nn.LayerNorm((dims,))
... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
harrisonvanderbyl/rwkvstic | https://github.com/harrisonvanderbyl/rwkvstic/blob/55826c8d0bdf89ba36bc4eb6518fa736aa479913/src/rwkvstic/training/modules/wkv.py | src/rwkvstic/training/modules/wkv.py | import torch
class wkv_power(torch.nn.Module):
def __init__(self, dims, T):
super(wkv_power, self).__init__()
self.time_first = torch.nn.Parameter(torch.randn(dims))
self.time_decay = torch.nn.Parameter(torch.randn(dims))
self.T = T
self.register_parameter("time_fi... | python | MIT | 55826c8d0bdf89ba36bc4eb6518fa736aa479913 | 2026-01-05T07:11:22.708425Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/setup.py | setup.py | from setuptools import setup, find_packages
with open("README.md", "r", encoding="utf-8") as f:
long_description = f.read()
setup(
name="BiFuncLib",
version="1.0.0",
description="A Python library for biclustering with functional data",
author="Yuhao Zhong",
author_email="Barry57@163.com",
... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/docs/source/conf.py | docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/cc_main_func.py | BiFuncLib/cc_main_func.py | import numpy as np
from scipy.optimize import minimize_scalar
from scipy.interpolate import interp1d
from scipy.spatial.distance import pdist, squareform
def medoid_evaluation(fun_mat, a, b, const_a, const_b):
n, m, p = fun_mat.shape
fun_per_medoid = fun_mat.reshape(n * m, p)
distance = squareform(pdist(f... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/simulation_data.py | BiFuncLib/simulation_data.py | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import multivariate_normal, norm
from pathlib import Path
from GENetLib.fda_func import bspline_mat
from GENetLib.fda_func import create_bspline_basis
from GENetLib.fda_func import fd
from GENetLib.fda_func import eval_fd
from BiFu... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/FDPlot.py | BiFuncLib/FDPlot.py | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import gridspec
from GENetLib.fda_func import bspline_mat
from GENetLib.fda_func import eval_basis, eval_fd
from GENetLib.fda_func import create_bspline_basis, create_fourier_basis
from GENetLib.plot_gene import plot_fd
import seabor... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | true |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/pf_bifunc.py | BiFuncLib/pf_bifunc.py | import numpy as np
import pandas as pd
import networkx as nx
from GENetLib.fda_func import bspline_mat
from scipy.linalg import solve
from BiFuncLib.pf_main_func import inv_uty_cal, beta_ini_cal, biclustr_admm
from BiFuncLib.AuxFunc import AuxFunc
def pf_bifunc(
data,
nknots,
order,
gamma1,
gamma... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/sas_main_func.py | BiFuncLib/sas_main_func.py | import numpy as np
from GENetLib.fda_func import create_bspline_basis, eval_basis, fdpar
from sklearn.mixture import GaussianMixture
from scipy.cluster.hierarchy import linkage, fcluster
from scipy.sparse import csr_matrix
from GENetLib.BsplineFunc import BsplineFunc
def get_sigma(x, curve, time, S, piigivej, gcov, ... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/cvx_biclus.py | BiFuncLib/cvx_biclus.py | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from BiFuncLib.cvx_main_func import cobra_validate, cobra_pod, biclust_smooth
warnings.filterwarnings(
"ignore", category=RuntimeWarning, message="Mean of empty slice"
)
# Cluster with validation
def cvx_biclus_valid(
... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/cvx_main_func.py | BiFuncLib/cvx_main_func.py | import numpy as np
from math import exp, sqrt
from scipy.sparse import csc_matrix, coo_matrix
import networkx as nx
import random
import math
def kernel_weights(X, phi):
p, n = X.shape
num_weights = n * (n - 1) // 2
w = np.empty(num_weights, dtype=float)
k = 0
for i in range(n - 1):
for j ... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/cc_bifunc.py | BiFuncLib/cc_bifunc.py | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from BiFuncLib.cc_main_func import bigcc_fun, evaluate_mat_dist, ccscore_fun
from BiFuncLib.bimax_biclus import bimax_biclus
# Functional Cheng and Church algorithm
def cc_bifunc(
data,
delta,
theta=1,
template_type="mean",
n... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/sas_bifunc.py | BiFuncLib/sas_bifunc.py | import numpy as np
import itertools
from GENetLib.fda_func import fd
from GENetLib.plot_gene import plot_fd
from GENetLib.fda_func import create_bspline_basis
from BiFuncLib.sas_main_func import (
sasfclust_init,
loglik,
get_msdrule,
get_zero,
sasfclust_Mstep,
sasfclust_Estep,
classify,
)
... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/lbm_main_func.py | BiFuncLib/lbm_main_func.py | import numpy as np
from sklearn.cluster import KMeans
from scipy.linalg import cholesky, solve, eig
import copy
from GENetLib.fda_func import create_bspline_basis, create_fourier_basis
from GENetLib.fda_func import inprod
from GENetLib.BsplineFunc import BsplineFunc
from BiFuncLib.fem_bifunc import fem_bifunc
def ar... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/local_main_func.py | BiFuncLib/local_main_func.py | import numpy as np
from GENetLib.fda_func import create_bspline_basis
from GENetLib.fda_func import eval_basis
from sklearn.cluster import KMeans
from scipy.linalg import norm
from itertools import combinations
import networkx as nx
import math
from GENetLib.BsplineFunc import BsplineFunc
from BiFuncLib.AuxFunc import... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/AuxFunc.py | BiFuncLib/AuxFunc.py | import numpy as np
import pandas as pd
import itertools
from scipy.sparse import lil_matrix
import matplotlib.pyplot as plt
class AuxFunc:
def __init__(self, n, m=None, x=None, V=None):
self.n = n
self.m = m if m is not None else None
self.x = np.array(x) if x is not None else None
... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/fem_bifunc.py | BiFuncLib/fem_bifunc.py | import numpy as np
import warnings
from BiFuncLib.fem_main_func import fem_main_func
warnings.filterwarnings("ignore", category=RuntimeWarning)
# Model-based clustering with the funFEM algorithm
def fem_bifunc(
fd,
K=np.arange(2, 7),
model=["AkjBk"],
crit="bic",
init="kmeans",
Tinit=(),
... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/sparse_main_func.py | BiFuncLib/sparse_main_func.py | import numpy as np
import math
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import linkage, fcluster
from sklearn_extra.cluster import KMedoids
# Classification error rate function
def cer(P, Q):
if len(P) != len(Q):
raise ValueError("The two partitions must have the same length")
c... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/sparse_bifunc.py | BiFuncLib/sparse_bifunc.py | from BiFuncLib.sparse_main_func import (
FKMSparseClustering_permute,
FKMSparseClustering,
cer,
)
def sparse_bifunc(data, x, K, method="kmea", true_clus=None):
mscelto = FKMSparseClustering_permute(data.T, x, K, method=method)["m"]
result = FKMSparseClustering(data.T, x, K, mscelto, method)
if... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/ssvd_main_func.py | BiFuncLib/ssvd_main_func.py | import numpy as np
import pandas as pd
import math
from copy import deepcopy
from scipy.sparse.linalg import svds
import gc
from BiFuncLib.BiclustResult import BiclustResult
def thresh(z, delta, thredtype=1, a=3.7):
z = np.asarray(z)
if thredtype == 1:
return (
np.sign(z)
* ((... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/local_bifunc.py | BiFuncLib/local_bifunc.py | import numpy as np
from BiFuncLib.local_main_func import calculate_gcv, calculate_bic, local_admm
def local_bifunc(
data,
times,
lambda1,
lambda2,
lambda3,
opt=False,
rangeval=(0, 1),
nknots=30,
order=4,
nu=2,
tau=3,
K0=6,
rep_num=100,
kappa=1,
eps_outer=0.... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/fem_main_func.py | BiFuncLib/fem_main_func.py | import numpy as np
import matplotlib.pyplot as plt
from numpy.linalg import inv, svd, pinv
from sklearn.cluster import KMeans
from sklearn.linear_model import ElasticNet
from GENetLib.fda_func import inprod
from scipy.cluster.hierarchy import linkage, cut_tree
def criteria(loglik, T, prms, n):
K = prms["K"]
p... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/bimax_biclus.py | BiFuncLib/bimax_biclus.py | import pandas as pd
from BiFuncLib.BiclustResult import BiclustResult
def apriori_bimax(matrix, minr=2, minc=2, number=100):
rows = len(matrix)
cols = len(matrix[0]) if rows else 0
row_masks = []
for row in matrix:
m = 0
for j, v in enumerate(row):
if v:
m ... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/__init__.py | BiFuncLib/__init__.py | from .simulation_data import (
pf_sim_data,
local_sim_data,
cc_sim_data,
lbm_sim_data,
)
from .simulation_data import (
sas_sim_data,
sparse_sim_data,
cvx_sim_data,
ssvd_sim_data,
)
from .BsplineFunc import BsplineFunc
from .BiclustResult import BiclustResult
from .bcheatmap import bchea... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/pf_main_func.py | BiFuncLib/pf_main_func.py | import numpy as np
from scipy.linalg import block_diag
from itertools import combinations
# Generate the initial values of Beta
def beta_ini_cal(oridata_list, Y_list, D_d, n, q, p, gamma1):
Beta_list = []
for i in range(n):
Beta_i = []
for j in range(q):
data_ij = np.array(oridata_... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/lbm_bifunc.py | BiFuncLib/lbm_bifunc.py | import itertools
import numpy as np
from BiFuncLib.lbm_main_func import lbm_main_func
# Model-based clustering with the funLBM algorithm
def lbm_bifunc(
data,
K,
L,
maxit=50,
burn=25,
basis_name="fourier",
nbasis=15,
gibbs_it=3,
display=False,
init="funFEM",
):
if not hasa... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/BsplineFunc.py | BiFuncLib/BsplineFunc.py | import numpy as np
from GENetLib.fda_func import fd, inprod, eval_basis, int2lfd
from GENetLib.fda_func import ppbspline, ycheck, wtcheck, fdparcheck
from scipy.linalg import cholesky, solve, qr, eigh, lstsq
# Create a class for b-spline functions
class BsplineFunc:
def __init__(self, basisobj, Lfdobj=2, rng=None... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/BiclustResult.py | BiFuncLib/BiclustResult.py | import numpy as np
class BiclustResult:
def __init__(self, params, RowxNumber, NumberxCol, Number, info):
self.params = params
self.RowxNumber = RowxNumber
self.NumberxCol = NumberxCol.T
self.Number = Number
self.info = info
self.cluster_row_sizes = np.sum(RowxNumbe... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/bcheatmap.py | BiFuncLib/bcheatmap.py | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib as mpl
from matplotlib.colors import LinearSegmentedColormap
def ma_palette(low="blue", mid="white", high="red", k=50):
if mid is None:
colors = [low, high]
else:
color... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/BiFuncLib/ssvd_biclus.py | BiFuncLib/ssvd_biclus.py | from BiFuncLib.ssvd_main_func import ssvd_bc, s4vd
def s4vd_biclus(
data,
steps=100,
pcerv=0.1,
pceru=0.1,
ss_thr=(0.6, 0.65),
size=0.5,
gamm=0,
iters=100,
nbiclust=10,
merr=1e-3,
cols_nc=True,
rows_nc=True,
row_overlap=True,
col_overlap=True,
row_min=1,
... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/funlbm_test.py | pytest/funlbm_test.py | import numpy as np
import pytest
from BiFuncLib.FDPlot import FDPlot
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from BiFuncLib.simulation_data import lbm_sim_data
from BiFuncLib.lbm_bifunc import lbm_bifunc
from BiFuncLib.lbm_main_func import ari
def _check_lbm_result(res: dict):
ass... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/bimax_test.py | pytest/bimax_test.py | import matplotlib
matplotlib.use("Agg")
import pytest
from BiFuncLib.simulation_data import bimax_sim_data
from BiFuncLib.bimax_biclus import bimax_biclus
from BiFuncLib.bcheatmap import bcheatmap
def test_bimax_full_story():
bimax_simdata = bimax_sim_data()
bimax_res = bimax_biclus(
bimax_simdata,
... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/funsas_test.py | pytest/funsas_test.py | import numpy as np
import matplotlib
matplotlib.use("Agg")
from BiFuncLib.simulation_data import sas_sim_data
from BiFuncLib.sas_bifunc import sas_bifunc, sas_bifunc_cv
from BiFuncLib.FDPlot import FDPlot
def _check_sas_result(res):
assert isinstance(res, dict)
def test_sas_full_story():
sas_simdata_0 = sa... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/bcheatmap_test.py | pytest/bcheatmap_test.py | import numpy as np
import pandas as pd
import pytest
import matplotlib.pyplot as plt
from unittest.mock import patch
from BiFuncLib.bcheatmap import bcheatmap, ma_palette
@pytest.fixture(scope="session", autouse=True)
def no_show():
with patch.object(plt, "show"):
yield
@pytest.fixture
def X():
np.r... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/funsparse_test.py | pytest/funsparse_test.py | import numpy as np
import pytest
import matplotlib
matplotlib.use("Agg")
from BiFuncLib.simulation_data import sparse_sim_data
from BiFuncLib.sparse_bifunc import sparse_bifunc
from BiFuncLib.FDPlot import FDPlot
def _check_sparse_result(res):
assert isinstance(res, dict)
def test_sparse_sim_data():
n = 10... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/ssvd_test.py | pytest/ssvd_test.py | import matplotlib
matplotlib.use("Agg")
import pytest
from BiFuncLib.simulation_data import ssvd_sim_data
from BiFuncLib.ssvd_main_func import jaccardmat
from BiFuncLib.ssvd_biclus import ssvd_biclus, s4vd_biclus
from BiFuncLib.bcheatmap import bcheatmap
from BiFuncLib.BiclustResult import BiclustResult
@pytest.fixt... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/cvx_test.py | pytest/cvx_test.py | import math
import random
import numpy as np
import pytest
import matplotlib
matplotlib.use("Agg")
from BiFuncLib.simulation_data import cvx_sim_data
from BiFuncLib.cvx_main_func import gkn_weights
from BiFuncLib.cvx_biclus import cvx_biclus_valid, cvx_biclus_missing
@pytest.fixture(scope="session")
def data():
... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/bspline_test.py | pytest/bspline_test.py | import numpy as np
import pytest
from BiFuncLib.BsplineFunc import BsplineFunc
from GENetLib.fda_func import create_bspline_basis, create_fourier_basis
@pytest.fixture
def sample_data():
n = 50
argvals = np.linspace(0, 1, n)
y = np.sin(2 * np.pi * argvals) + 0.1 * np.random.randn(n)
basis = create_bsp... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/funfem_test.py | pytest/funfem_test.py | import pytest
import numpy as np
from GENetLib.fda_func import create_fourier_basis
from BiFuncLib.fem_bifunc import fem_bifunc
from BiFuncLib.simulation_data import fem_sim_data
from BiFuncLib.BsplineFunc import BsplineFunc
from GENetLib.fda_func import basis_fd
from BiFuncLib.FDPlot import FDPlot
import matplotlib
m... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/funcc_test.py | pytest/funcc_test.py | import numpy as np
import pandas as pd
import pytest
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from BiFuncLib.simulation_data import cc_sim_data
from BiFuncLib.cc_bifunc import cc_bifunc, cc_bifunc_cv
from BiFuncLib.FDPlot import FDPlot
def _check_cc_result(res):
assert isinstance(r... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/funpf_test.py | pytest/funpf_test.py | import pytest
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from BiFuncLib.pf_bifunc import pf_bifunc
from BiFuncLib.simulation_data import pf_sim_data
from BiFuncLib.FDPlot import FDPlot
def _check_pf_result(res):
assert isinstance(res, dict)
... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
XMU-Kuangnan-Fang-Team/BiFuncLib | https://github.com/XMU-Kuangnan-Fang-Team/BiFuncLib/blob/e7879c4e3c425655ee2aefd1c66c3434a27f068e/pytest/funlocal_test.py | pytest/funlocal_test.py | import numpy as np
import matplotlib
matplotlib.use("Agg")
from BiFuncLib.local_bifunc import local_bifunc
from BiFuncLib.simulation_data import local_sim_data
from BiFuncLib.FDPlot import FDPlot
def _check_local_result(res):
assert isinstance(res, dict)
def test_local_full_story():
local_simdata = local_s... | python | MIT | e7879c4e3c425655ee2aefd1c66c3434a27f068e | 2026-01-05T07:11:22.982774Z | false |
williballenthin/process-forest | https://github.com/williballenthin/process-forest/blob/6c7e93a88c8ca24bedebc83a80636351652bccff/setup.py | setup.py | #! /usr/bin/env python
from setuptools import setup, find_packages
setup(
name='process-forest',
author='Willi Ballenthin',
version='0.1',
packages = find_packages(),
install_requires=[
'iso8601',
'lxml',
'python-evtx',
'pytz'
],
scripts=[
'src/proce... | python | Apache-2.0 | 6c7e93a88c8ca24bedebc83a80636351652bccff | 2026-01-05T07:11:27.359579Z | false |
williballenthin/process-forest | https://github.com/williballenthin/process-forest/blob/6c7e93a88c8ca24bedebc83a80636351652bccff/src/process_forest.py | src/process_forest.py | #!/usr/bin/env python
import logging
import datetime
from collections import namedtuple
import pytz
import json
import iso8601
from lxml import etree
from lxml.etree import XMLSyntaxError
from Evtx.Evtx import Evtx
from Evtx.Views import evtx_file_xml_view
g_logger = logging.getLogger("process-forest.global")
de... | python | Apache-2.0 | 6c7e93a88c8ca24bedebc83a80636351652bccff | 2026-01-05T07:11:27.359579Z | false |
wmylxmj/Anime-Super-Resolution | https://github.com/wmylxmj/Anime-Super-Resolution/blob/c854227efcee961151721032c0ecfc4317f989e4/train.py | train.py | # -*- coding: utf-8 -*-
"""
Created on Sun Apr 28 18:32:15 2019
@author: wmy
"""
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
from keras import backend as K
from keras.losses import mean_absolute_error, mean_squared_error
from keras.models import load_model
from ker... | python | MIT | c854227efcee961151721032c0ecfc4317f989e4 | 2026-01-05T07:09:39.829800Z | false |
wmylxmj/Anime-Super-Resolution | https://github.com/wmylxmj/Anime-Super-Resolution/blob/c854227efcee961151721032c0ecfc4317f989e4/model.py | model.py | # -*- coding: utf-8 -*-
"""
Created on Sun Apr 28 18:29:04 2019
@author: wmy
"""
import numpy as np
import tensorflow as tf
from keras import backend as K
from keras.layers import Add, Conv2D, Input, Lambda, Activation
from keras.models import Model
def SubpixelConv2D(scale, **kwargs):
return Lambda(lambda x: tf... | python | MIT | c854227efcee961151721032c0ecfc4317f989e4 | 2026-01-05T07:09:39.829800Z | false |
wmylxmj/Anime-Super-Resolution | https://github.com/wmylxmj/Anime-Super-Resolution/blob/c854227efcee961151721032c0ecfc4317f989e4/optimizer.py | optimizer.py | # -*- coding: utf-8 -*-
"""
Created on Wed Apr 24 21:30:14 2019
@author: wmy
"""
import tensorflow as tf
from keras import backend as K
from keras.optimizers import Adam
from tqdm import tqdm
class AdamWithWeightsNormalization(Adam):
def get_updates(self, loss, params):
grads = self.get_gradients(lo... | python | MIT | c854227efcee961151721032c0ecfc4317f989e4 | 2026-01-05T07:09:39.829800Z | false |
wmylxmj/Anime-Super-Resolution | https://github.com/wmylxmj/Anime-Super-Resolution/blob/c854227efcee961151721032c0ecfc4317f989e4/utils.py | utils.py | # -*- coding: utf-8 -*-
"""
Created on Sun Apr 28 15:36:37 2019
@author: wmy
"""
import numpy as np
import matplotlib.pyplot as plt
import os
import random
from PIL import Image
from PIL import ImageFilter
class DataLoader(object):
def __init__(self, scale=4, crop_size=96, name=None):
self.__scale =... | python | MIT | c854227efcee961151721032c0ecfc4317f989e4 | 2026-01-05T07:09:39.829800Z | false |
wmylxmj/Anime-Super-Resolution | https://github.com/wmylxmj/Anime-Super-Resolution/blob/c854227efcee961151721032c0ecfc4317f989e4/evaluate.py | evaluate.py | # -*- coding: utf-8 -*-
"""
Created on Tue Apr 30 21:24:36 2019
@author: wmy
"""
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
from keras import backend as K
from keras.losses import mean_absolute_error, mean_squared_error
from keras.models import load_model
from ker... | python | MIT | c854227efcee961151721032c0ecfc4317f989e4 | 2026-01-05T07:09:39.829800Z | false |
wmylxmj/Anime-Super-Resolution | https://github.com/wmylxmj/Anime-Super-Resolution/blob/c854227efcee961151721032c0ecfc4317f989e4/predict.py | predict.py | # -*- coding: utf-8 -*-
"""
Created on Tue Apr 30 20:28:04 2019
@author: wmy
"""
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
from keras import backend as K
from keras.losses import mean_absolute_error, mean_squared_error
from keras.models import load_model
from ker... | python | MIT | c854227efcee961151721032c0ecfc4317f989e4 | 2026-01-05T07:09:39.829800Z | false |
xdxie/WordArt | https://github.com/xdxie/WordArt/blob/89bf8a218881b250d0ead7a0287526c69586c92a/setup.py | setup.py | import os
import os.path as osp
import shutil
import sys
import warnings
from setuptools import find_packages, setup
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
version_file = 'mmocr/version.py'
is_windows = sys.platform == 'win32'
def add_mim_ext... | python | Apache-2.0 | 89bf8a218881b250d0ead7a0287526c69586c92a | 2026-01-05T07:11:30.009719Z | false |
xdxie/WordArt | https://github.com/xdxie/WordArt/blob/89bf8a218881b250d0ead7a0287526c69586c92a/tools/train.py | tools/train.py | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
import torch.distributed as dist
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist, set_random_seed
f... | python | Apache-2.0 | 89bf8a218881b250d0ead7a0287526c69586c92a | 2026-01-05T07:11:30.009719Z | false |
xdxie/WordArt | https://github.com/xdxie/WordArt/blob/89bf8a218881b250d0ead7a0287526c69586c92a/tools/publish_model.py | tools/publish_model.py | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_... | python | Apache-2.0 | 89bf8a218881b250d0ead7a0287526c69586c92a | 2026-01-05T07:11:30.009719Z | false |
xdxie/WordArt | https://github.com/xdxie/WordArt/blob/89bf8a218881b250d0ead7a0287526c69586c92a/tools/benchmark_processing.py | tools/benchmark_processing.py | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
"""This file is for benchmark data loading process. It can also be used to
refresh the memcached cache. The command line to run this file is:
$ python -m cProfile -o program.prof tools/analysis/benchmark_processing.py
configs/task/method/[config fil... | python | Apache-2.0 | 89bf8a218881b250d0ead7a0287526c69586c92a | 2026-01-05T07:11:30.009719Z | false |
xdxie/WordArt | https://github.com/xdxie/WordArt/blob/89bf8a218881b250d0ead7a0287526c69586c92a/tools/kie_test_imgs.py | tools/kie_test_imgs.py | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import ast
import os
import os.path as osp
import mmcv
import numpy as np
import torch
from mmcv import Config
from mmcv.image import tensor2imgs
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint
from ... | python | Apache-2.0 | 89bf8a218881b250d0ead7a0287526c69586c92a | 2026-01-05T07:11:30.009719Z | false |
xdxie/WordArt | https://github.com/xdxie/WordArt/blob/89bf8a218881b250d0ead7a0287526c69586c92a/tools/analyze_logs.py | tools/analyze_logs.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Modified from https://github.com/open-
mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py."""
import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def cal_train_time(... | python | Apache-2.0 | 89bf8a218881b250d0ead7a0287526c69586c92a | 2026-01-05T07:11:30.009719Z | false |
xdxie/WordArt | https://github.com/xdxie/WordArt/blob/89bf8a218881b250d0ead7a0287526c69586c92a/tools/recog_test_imgs.py | tools/recog_test_imgs.py | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import shutil
import time
from argparse import ArgumentParser
from itertools import compress
import mmcv
from mmcv.utils import ProgressBar
from mmocr.apis import init_detector, model_inference
from mmocr.core.evaluation.ocr_m... | python | Apache-2.0 | 89bf8a218881b250d0ead7a0287526c69586c92a | 2026-01-05T07:11:30.009719Z | false |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.