vectorization

问题 I have two numpy arrays image and warped_image and indices arrays ix,iy . I need to add image to warped_image such that image[i,j] is added to warped_image[iy[i,j],ix[i,j]] . The below code works if the pairs (iy[i,j], ix[i,j]) are unique for all i,j . But when they are not unique i.e. when 2 elements from image need to be added to the same element in warped_image , only one of them gets added. How can I add both elements from image to the same element in warped_image ? Note that, I don't

问题 I have two numpy arrays image and warped_image and indices arrays ix,iy . I need to add image to warped_image such that image[i,j] is added to warped_image[iy[i,j],ix[i,j]] . The below code works if the pairs (iy[i,j], ix[i,j]) are unique for all i,j . But when they are not unique i.e. when 2 elements from image need to be added to the same element in warped_image , only one of them gets added. How can I add both elements from image to the same element in warped_image ? Note that, I don't

问题 I have two numpy arrays, X and Y , with shapes (n,d) and (m,d) , respectively. Assume that we want to compute the Euclidean distances between each row of X and each row of Y and store the result in array Z with shape (n,m) . I have two implementations for this. The first implementation uses two for loops as follows: for i in range(n): for j in range(m): Z[i,j] = np.sqrt(np.sum(np.square(X[i] - Y[j]))) The second implementation uses only one loop by vectorization: for i in range(n): Z[i] = np

问题 I have two numpy arrays, X and Y , with shapes (n,d) and (m,d) , respectively. Assume that we want to compute the Euclidean distances between each row of X and each row of Y and store the result in array Z with shape (n,m) . I have two implementations for this. The first implementation uses two for loops as follows: for i in range(n): for j in range(m): Z[i,j] = np.sqrt(np.sum(np.square(X[i] - Y[j]))) The second implementation uses only one loop by vectorization: for i in range(n): Z[i] = np

问题 This question is really a curiosity. I was converting a routine into SIMD instructions (and I am quite new to SIMD programming), and had trouble with the following bit of code: // args: uint32_t phase_current; uint32_t phase_increment; uint32_t phase_increment_step; for (int i = 0; i < blockSize; ++i) { USEFUL_FUNC(phase_current); phase_increment += phase_increment_step; phase_current += phase_increment; } The Question: Assuming that USEFUL_FUNC has a SIMD implementation and I am simply

问题 This question is really a curiosity. I was converting a routine into SIMD instructions (and I am quite new to SIMD programming), and had trouble with the following bit of code: // args: uint32_t phase_current; uint32_t phase_increment; uint32_t phase_increment_step; for (int i = 0; i < blockSize; ++i) { USEFUL_FUNC(phase_current); phase_increment += phase_increment_step; phase_current += phase_increment; } The Question: Assuming that USEFUL_FUNC has a SIMD implementation and I am simply

问题 This question is really a curiosity. I was converting a routine into SIMD instructions (and I am quite new to SIMD programming), and had trouble with the following bit of code: // args: uint32_t phase_current; uint32_t phase_increment; uint32_t phase_increment_step; for (int i = 0; i < blockSize; ++i) { USEFUL_FUNC(phase_current); phase_increment += phase_increment_step; phase_current += phase_increment; } The Question: Assuming that USEFUL_FUNC has a SIMD implementation and I am simply

问题 I have a dataframe where the first row is the initial condition. df = pd.DataFrame({"Year": np.arange(4), "Pop": [0.4] + [np.nan]* 3}) and a function f(x,r) = r*x*(1-x) , where r = 2 is a constant and 0 <= x <= 1 . I want to produce the following dataframe by applying the function to column Pop row-by-row iteratively. I.e., df.Pop[i] = f(df.Pop[i-1], r=2) df = pd.DataFrame({"Year": np.arange(4), "Pop": [0.4, 0.48, 4992, 0.49999872]}) Question: Is it possible to do this in a vectorized way? I

问题 I have a dataframe where the first row is the initial condition. df = pd.DataFrame({"Year": np.arange(4), "Pop": [0.4] + [np.nan]* 3}) and a function f(x,r) = r*x*(1-x) , where r = 2 is a constant and 0 <= x <= 1 . I want to produce the following dataframe by applying the function to column Pop row-by-row iteratively. I.e., df.Pop[i] = f(df.Pop[i-1], r=2) df = pd.DataFrame({"Year": np.arange(4), "Pop": [0.4, 0.48, 4992, 0.49999872]}) Question: Is it possible to do this in a vectorized way? I

问题 I want to translate following routine from class [Word2VecModel]https://github.com/apache/spark/blob/branch-2.3/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala into pyspark. override def transform(dataset: Dataset[_]): DataFrame = { transformSchema(dataset.schema, logging = true) val vectors = wordVectors.getVectors .mapValues(vv => Vectors.dense(vv.map(_.toDouble))) .map(identity) // mapValues doesn't return a serializable map (SI-7005) val bVectors = dataset.sparkSession